code: def test__flag_jumps_single_col_percentage_scale_flags_large_jump(
      timeseries_dataframe,
  ):
      """
      Test utility function for flagging jumps with percentage scale and
      a 100% jump. Here, a large threshold is used to verify only
      drastically changed rows are flagged.
      """
      # Setup
      df = timeseries_dataframe
  
      # Exercise
      result_incr = _flag_jumps_single_col(
          df,
          "col5",
          scale="percentage",
          direction="increasing",
          threshold=1.0,
      )
      result_decr = _flag_jumps_single_col(
          df,
          "col5",
          scale="percentage",
          direction="decreasing",
          threshold=1.0,
      )
      result_any = _flag_jumps_single_col(
          df, "col5", scale="percentage", direction="any", threshold=1.0
      )
  
      # Verify
      np.testing.assert_array_equal(
          result_incr.array, [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
      )
      np.testing.assert_array_equal(
          result_decr.array, [0, 0, 0, 0, 0, 0, 0, 1, 0, 1]
      )
      np.testing.assert_array_equal(
          result_any.array, [0, 0, 0, 1, 0, 0, 0, 1, 0, 1]
      )
  
imports: import numpy
  import pandas
  import pytest
  import janitor.timeseries
  from janitor.errors import JanitorError
  from janitor.timeseries import _flag_jumps_single_col
label_desc: Use image node_id 12 for calling a global function with example usage: test__flag_jumps_single_col_percentage_scale_flags_large_jump(timeseries_dataframe) without return types