nodes: 0, module; 1, function_definition; 2, function_name:isect; 3, parameters; 4, block; 5, identifier:list1; 6, identifier:list2; 7, expression_statement; 8, expression_statement; 9, return_statement; 10, comment:r"""
      returns list1 elements that are also in list2. preserves order of list1
  
      intersect_ordered
  
      Args:
          list1 (list):
          list2 (list):
  
      Returns:
          list: new_list
  
      Example:
          >>> # DISABLE_DOCTEST
          >>> from utool.util_list import *  # NOQA
          >>> list1 = ['featweight_rowid', 'feature_rowid', 'config_rowid', 'featweight_forground_weight']
          >>> list2 = [u'featweight_rowid']
          >>> result = intersect_ordered(list1, list2)
          >>> print(result)
          ['featweight_rowid']
  
      Timeit:
          def timeit_func(func, *args):
              niter = 10
              times = []
              for count in range(niter):
                  with ut.Timer(verbose=False) as t:
                      _ = func(*args)
                  times.append(t.ellapsed)
              return sum(times) / niter
  
          grid = {
              'size1': [1000, 5000, 10000, 50000],
              'size2': [1000, 5000, 10000, 50000],
              #'overlap': [0, 1],
          }
          data = []
          for kw in ut.all_dict_combinations(grid):
              pool = np.arange(kw['size1'] * 2)
              size2 = size1 = kw['size1']
              size2 = kw['size2']
              list1 = (np.random.rand(size1) * size1).astype(np.int32).tolist()
              list1 = ut.random_sample(pool, size1).tolist()
              list2 = ut.random_sample(pool, size2).tolist()
              list1 = set(list1)
              list2 = set(list2)
              kw['ut'] = timeit_func(ut.isect, list1, list2)
              #kw['np1'] = timeit_func(np.intersect1d, list1, list2)
              #kw['py1'] = timeit_func(lambda a, b: set.intersection(set(a), set(b)), list1, list2)
              kw['py2'] = timeit_func(lambda a, b: sorted(set.intersection(set(a), set(b))), list1, list2)
              data.append(kw)
  
          import pandas as pd
          pd.options.display.max_rows = 1000
          pd.options.display.width = 1000
          df = pd.DataFrame.from_dict(data)
          data_keys = list(grid.keys())
          other_keys = ut.setdiff(df.columns, data_keys)
          df = df.reindex_axis(data_keys + other_keys, axis=1)
          df['abs_change'] = df['ut'] - df['py2']
          df['pct_change'] = df['abs_change'] / df['ut'] * 100
          #print(df.sort('abs_change', ascending=False))
  
          print(str(df).split('\n')[0])
          for row in df.values:
              argmin = row[len(data_keys):len(data_keys) + len(other_keys)].argmin() + len(data_keys)
              print('    ' + ', '.join([
              '%6d' % (r) if x < len(data_keys) else (
                  ut.color_text('%8.6f' % (r,), 'blue')
                      if x == argmin else '%8.6f' % (r,))
              for x, r in enumerate(row)
              ]))
  
          %timeit ut.isect(list1, list2)
          %timeit np.intersect1d(list1, list2, assume_unique=True)
          %timeit set.intersection(set(list1), set(list2))
  
          #def highlight_max(s):
          #    '''
          #    highlight the maximum in a Series yellow.
          #    '''
          #    is_max = s == s.max()
          #    return ['background-color: yellow' if v else '' for v in is_max]
          #df.style.apply(highlight_max)
      """; 11, assignment; 12, list_comprehension; 13, identifier:set2; 14, call; 15, identifier:item; 16, for_in_clause; 17, if_clause; 18, identifier:set; 19, argument_list; 20, identifier:item; 21, identifier:list1; 22, comparison_operator:item in set2; 23, identifier:list2; 24, identifier:item; 25, identifier:set2
code: def isect(list1, list2):
      r"""
      returns list1 elements that are also in list2. preserves order of list1
  
      intersect_ordered
  
      Args:
          list1 (list):
          list2 (list):
  
      Returns:
          list: new_list
  
      Example:
          >>> # DISABLE_DOCTEST
          >>> from utool.util_list import *  # NOQA
          >>> list1 = ['featweight_rowid', 'feature_rowid', 'config_rowid', 'featweight_forground_weight']
          >>> list2 = [u'featweight_rowid']
          >>> result = intersect_ordered(list1, list2)
          >>> print(result)
          ['featweight_rowid']
  
      Timeit:
          def timeit_func(func, *args):
              niter = 10
              times = []
              for count in range(niter):
                  with ut.Timer(verbose=False) as t:
                      _ = func(*args)
                  times.append(t.ellapsed)
              return sum(times) / niter
  
          grid = {
              'size1': [1000, 5000, 10000, 50000],
              'size2': [1000, 5000, 10000, 50000],
              #'overlap': [0, 1],
          }
          data = []
          for kw in ut.all_dict_combinations(grid):
              pool = np.arange(kw['size1'] * 2)
              size2 = size1 = kw['size1']
              size2 = kw['size2']
              list1 = (np.random.rand(size1) * size1).astype(np.int32).tolist()
              list1 = ut.random_sample(pool, size1).tolist()
              list2 = ut.random_sample(pool, size2).tolist()
              list1 = set(list1)
              list2 = set(list2)
              kw['ut'] = timeit_func(ut.isect, list1, list2)
              #kw['np1'] = timeit_func(np.intersect1d, list1, list2)
              #kw['py1'] = timeit_func(lambda a, b: set.intersection(set(a), set(b)), list1, list2)
              kw['py2'] = timeit_func(lambda a, b: sorted(set.intersection(set(a), set(b))), list1, list2)
              data.append(kw)
  
          import pandas as pd
          pd.options.display.max_rows = 1000
          pd.options.display.width = 1000
          df = pd.DataFrame.from_dict(data)
          data_keys = list(grid.keys())
          other_keys = ut.setdiff(df.columns, data_keys)
          df = df.reindex_axis(data_keys + other_keys, axis=1)
          df['abs_change'] = df['ut'] - df['py2']
          df['pct_change'] = df['abs_change'] / df['ut'] * 100
          #print(df.sort('abs_change', ascending=False))
  
          print(str(df).split('\n')[0])
          for row in df.values:
              argmin = row[len(data_keys):len(data_keys) + len(other_keys)].argmin() + len(data_keys)
              print('    ' + ', '.join([
              '%6d' % (r) if x < len(data_keys) else (
                  ut.color_text('%8.6f' % (r,), 'blue')
                      if x == argmin else '%8.6f' % (r,))
              for x, r in enumerate(row)
              ]))
  
          %timeit ut.isect(list1, list2)
          %timeit np.intersect1d(list1, list2, assume_unique=True)
          %timeit set.intersection(set(list1), set(list2))
  
          #def highlight_max(s):
          #    '''
          #    highlight the maximum in a Series yellow.
          #    '''
          #    is_max = s == s.max()
          #    return ['background-color: yellow' if v else '' for v in is_max]
          #df.style.apply(highlight_max)
      """
      set2 = set(list2)
      return [item for item in list1 if item in set2]