code: def make_equity_minute_bar_data(cls):
      trading_calendar = cls.trading_calendars[Equity]
      # No data on first day.
      dts = trading_calendar.minutes_for_session(cls.trading_days[0])
      dfs = []
      dfs.append(
          pd.DataFrame(
              {
                  "open": full(len(dts), nan),
                  "high": full(len(dts), nan),
                  "low": full(len(dts), nan),
                  "close": full(len(dts), nan),
                  "volume": full(len(dts), 0),
              },
              index=dts,
          )
      )
      dts = trading_calendar.minutes_for_session(cls.trading_days[1])
      dfs.append(
          pd.DataFrame(
              {
                  "open": append(100.5, full(len(dts) - 1, nan)),
                  "high": append(100.9, full(len(dts) - 1, nan)),
                  "low": append(100.1, full(len(dts) - 1, nan)),
                  "close": append(100.3, full(len(dts) - 1, nan)),
                  "volume": append(1000, full(len(dts) - 1, nan)),
              },
              index=dts,
          )
      )
      dts = trading_calendar.minutes_for_session(cls.trading_days[2])
      dfs.append(
          pd.DataFrame(
              {
                  "open": [nan, 103.50, 102.50, 104.50, 101.50, nan],
                  "high": [nan, 103.90, 102.90, 104.90, 101.90, nan],
                  "low": [nan, 103.10, 102.10, 104.10, 101.10, nan],
                  "close": [nan, 103.30, 102.30, 104.30, 101.30, nan],
                  "volume": [0, 1003, 1002, 1004, 1001, 0],
              },
              index=dts[:6],
          )
      )
      dts = trading_calendar.minutes_for_session(cls.trading_days[3])
      dfs.append(
          pd.DataFrame(
              {
                  "open": full(len(dts), nan),
                  "high": full(len(dts), nan),
                  "low": full(len(dts), nan),
                  "close": full(len(dts), nan),
                  "volume": full(len(dts), 0),
              },
              index=dts,
          )
      )
      asset1_df = pd.concat(dfs)
      yield 1, asset1_df
  
      asset2_df = pd.DataFrame(
          {
              "open": 1.0055,
              "high": 1.0059,
              "low": 1.0051,
              "close": 1.0055,
              "volume": 100,
          },
          index=asset1_df.index,
      )
      yield 2, asset2_df
  
imports: from collections import OrderedDict
  from numpy import array, append, nan, full
  from numpy.testing import assert_almost_equal
  import pandas
  from pandas.tslib import Timedelta
  from catalyst.assets import Equity, Future
  from catalyst.data.data_portal import HISTORY_FREQUENCIES, OHLCV_FIELDS
  from catalyst.data.minute_bars import FUTURES_MINUTES_PER_DAY, US_EQUITIES_MINUTES_PER_DAY
  from catalyst.testing import parameter_space
  from catalyst.testing.fixtures import CatalystTestCase, WithTradingSessions, WithDataPortal, alias
  from catalyst.testing.predicates import assert_equal
  from catalyst.utils.numpy_utils import float64_dtype
label_desc: Use image node_id 3 for calling the DataPortalTestBase obj's underlying member method code with example usage: obj.make_equity_minute_bar_data(cls) without return types