code: def verify_regular_dt(self, idx, dt, mode, fields=None, assets=None):
      if mode == "daily":
          freq = "1d"
      else:
          freq = "1m"
  
      cal = self.trading_calendar
      equity_cal = self.trading_calendars[Equity]
  
      def reindex_to_primary_calendar(a, field):
          """
          Reindex an array of prices from a window on the NYSE
          calendar by the window on the primary calendar with the same
          dt and window size.
          """
          if mode == "daily":
              dts = cal.sessions_window(dt, -9)
  
              # `dt` may not be a session on the equity calendar, so
              # find the next valid session.
              equity_sess = equity_cal.minute_to_session_label(dt)
              equity_dts = equity_cal.sessions_window(equity_sess, -9)
          elif mode == "minute":
              dts = cal.minutes_window(dt, -10)
              equity_dts = equity_cal.minutes_window(dt, -10)
  
          output = pd.Series(
              index=equity_dts,
              data=a,
          ).reindex(dts)
  
          # Fill after reindexing, to ensure we don't forward fill
          # with values that are being dropped.
          if field == "volume":
              return output.fillna(0)
          elif field == "price":
              return output.fillna(method="ffill")
          else:
              return output
  
      fields = fields if fields is not None else ALL_FIELDS
      assets = (
          assets if assets is not None else [self.ASSET2, self.ASSET3]
      )
  
      bar_data = self.create_bardata(
          simulation_dt_func=lambda: dt,
      )
      check_internal_consistency(bar_data, assets, fields, 10, freq)
  
      for field in fields:
          for asset in assets:
              asset_series = bar_data.history(asset, field, 10, freq)
  
              base = MINUTE_FIELD_INFO[field] + 2
  
              if idx < 9:
                  missing_count = 9 - idx
                  present_count = 9 - missing_count
  
                  if field in OHLCP:
                      if asset == self.ASSET2:
                          # asset2 should have some leading nans
                          np.testing.assert_array_equal(
                              np.full(missing_count, np.nan),
                              asset_series[0:missing_count],
                          )
  
                          # asset2 should also have some real values
                          np.testing.assert_array_equal(
                              np.array(
                                  range(base, base + present_count + 1)
                              ),
                              asset_series[(9 - present_count) :],
                          )
  
                      if asset == self.ASSET3:
                          # asset3 should be NaN the entire time
                          np.testing.assert_array_equal(
                              np.full(10, np.nan), asset_series
                          )
                  elif field == "volume":
                      if asset == self.ASSET2:
                          # asset2 should have some zeros (instead of nans)
                          np.testing.assert_array_equal(
                              np.zeros(missing_count),
                              asset_series[0:missing_count],
                          )
  
                          # and some real values
                          np.testing.assert_array_equal(
                              np.array(
                                  range(base, base + present_count + 1)
                              )
                              * 100,
                              asset_series[(9 - present_count) :],
                          )
  
                      if asset == self.ASSET3:
                          # asset3 is all zeros, no volume yet
                          np.testing.assert_array_equal(
                              np.zeros(10), asset_series
                          )
              else:
                  # asset3 should have data every 10 minutes
                  # construct an array full of nans, put something in the
                  # right slot, and test for comparison
  
                  position_from_end = ((idx + 1) % 10) + 1
  
                  # asset3's baseline data is 9 NaNs, then 11, then 9 NaNs,
                  # then 21, etc.  for idx 9 to 19, value_for_asset3 should
                  # be a baseline of 11 (then adjusted for the individual
                  # field), thus the rounding down to the nearest 10.
                  value_for_asset3 = (
                      (((idx + 1) // 10) * 10)
                      + MINUTE_FIELD_INFO[field]
                      + 1
                  )
  
                  if field in OHLC:
                      asset3_answer_key = np.full(10, np.nan)
                      asset3_answer_key[
                          -position_from_end
                      ] = value_for_asset3
                      asset3_answer_key = reindex_to_primary_calendar(
                          asset3_answer_key,
                          field,
                      )
  
                      if asset == self.ASSET2:
                          np.testing.assert_array_equal(
                              reindex_to_primary_calendar(
                                  np.array(
                                      range(
                                          base + idx - 9, base + idx + 1
                                      )
                                  ),
                                  field,
                              ),
                              asset_series,
                          )
  
                      if asset == self.ASSET3:
                          np.testing.assert_array_equal(
                              asset3_answer_key, asset_series
                          )
                  elif field == "volume":
                      asset3_answer_key = np.zeros(10)
                      asset3_answer_key[-position_from_end] = (
                          value_for_asset3 * 100
                      )
                      asset3_answer_key = reindex_to_primary_calendar(
                          asset3_answer_key,
                          field,
                      )
  
                      if asset == self.ASSET2:
                          np.testing.assert_array_equal(
                              reindex_to_primary_calendar(
                                  np.array(
                                      range(
                                          base + idx - 9, base + idx + 1
                                      )
                                  )
                                  * 100,
                                  field,
                              ),
                              asset_series,
                          )
  
                      if asset == self.ASSET3:
                          np.testing.assert_array_equal(
                              asset3_answer_key, asset_series
                          )
                  elif field == "price":
                      # price is always forward filled
  
                      # asset2 has prices every minute, so it's easy
  
                      if asset == self.ASSET2:
                          # at idx 9, the data is 2 to 11
                          np.testing.assert_array_equal(
                              reindex_to_primary_calendar(
                                  range(idx - 7, idx + 3),
                                  field=field,
                              ),
                              asset_series,
                          )
  
                      if asset == self.ASSET3:
                          # Second part begins on the session after
                          # `position_from_end` on the NYSE calendar.
                          second_begin = dt - equity_cal.day * (
                              position_from_end - 1
                          )
  
                          # First part goes up until the start of the
                          # second part, because we forward-fill.
                          first_end = second_begin - cal.day
  
                          first_part = asset_series[:first_end]
                          second_part = asset_series[second_begin:]
  
                          decile_count = (idx + 1) // 10
  
                          # in our test data, asset3 prices will be nine
                          # NaNs, then ten 11s, ten 21s, ten 31s...
  
                          if len(second_part) >= 10:
                              np.testing.assert_array_equal(
                                  np.full(len(first_part), np.nan),
                                  first_part,
                              )
                          elif decile_count == 1:
                              np.testing.assert_array_equal(
                                  np.full(len(first_part), np.nan),
                                  first_part,
                              )
  
                              np.testing.assert_array_equal(
                                  np.array([11] * len(second_part)),
                                  second_part,
                              )
                          else:
                              np.testing.assert_array_equal(
                                  np.array(
                                      [decile_count * 10 - 9]
                                      * len(first_part)
                                  ),
                                  first_part,
                              )
  
                              np.testing.assert_array_equal(
                                  np.array(
                                      [decile_count * 10 + 1]
                                      * len(second_part)
                                  ),
                                  second_part,
                              )
  
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 7 for calling the WithHistory obj's underlying member method code with example usage: obj.verify_regular_dt(idx, dt, mode, fields, assets) and returns: output, output, output