alexland · October 26, 2021 14:04
diff --git a/crosstab.py b/crosstab.py
 def xtab(*cols, apply_wt=False):
  '''
  returns:
    (i) xt, NumPy array storing the xtab results, number of dimensions is equal to 
        the len(args) passed in
    (ii) unique_vals_all_cols, a tuple of 1D NumPy array for each dimension 
        in xt (for a 2D xtab, the tuple comprises the row and column headers)
    pass in:
      (i) 1 or more 1D NumPy arrays of integers
      (ii) if wts is True, then the last array in cols is an array of weights
      
  if return_inverse=True, then NP.unique also returns an integer index 
  (from 0, & of same len as array passed in) such that, uniq_vals[idx] gives the original array passed in

  higher dimensional cross tabulations are supported (eg, 2D & 3D)

  cross tabulation on two variables (columns):

  >>> q1 = NP.array([7, 8, 8, 8, 5, 6, 4, 6, 6, 8, 4, 6, 6, 6, 6, 8, 8, 5, 8, 6])
  >>> q2 = NP.array([6, 4, 6, 4, 8, 8, 4, 8, 7, 4, 4, 8, 8, 7, 5, 4, 8, 4, 4, 4])

  >>> uv, xt = xtab(q1, q2)
  >>> uv
    (array([4, 5, 6, 7, 8]), array([4, 5, 6, 7, 8]))

  >>> xt
    array([[2, 0, 0, 0, 0],
           [1, 0, 0, 0, 1],
           [1, 1, 0, 2, 4],
           [0, 0, 1, 0, 0],
           [5, 0, 1, 0, 1]], dtype=uint64)
    '''
  if not all(len(col) == len(cols[0]) for col in cols[1:]):
    raise ValueError("all arguments must be same size")

  if len(cols) == 0:
    raise TypeError("xtab() requires at least one argument")

  fnx1 = lambda q: len(q.squeeze().shape)
  if not all([fnx1(col) == 1 for col in cols]):
    raise ValueError("all input arrays must be 1D")

  if apply_wt:
    cols, wt = cols[:-1], cols[-1]
  else:
    wt = 1

  uniq_vals_all_cols, idx = zip( *(NP.unique(col, return_inverse=True) for col in cols) )
  shape_xt = [uniq_vals_col.size for uniq_vals_col in uniq_vals_all_cols]
  dtype_xt = 'float' if apply_wt else 'uint'
  xt = NP.zeros(shape_xt, dtype=dtype_xt)
  NP.add.at(xt, idx, wt)
  return uniq_vals_all_cols, xt
	def xtab(*cols, apply_wt=False):
	'''
	returns:
	(i) xt, NumPy array storing the xtab results, number of dimensions is equal to
	the len(args) passed in
	(ii) unique_vals_all_cols, a tuple of 1D NumPy array for each dimension
	in xt (for a 2D xtab, the tuple comprises the row and column headers)
	pass in:
	(i) 1 or more 1D NumPy arrays of integers
	(ii) if wts is True, then the last array in cols is an array of weights

	if return_inverse=True, then NP.unique also returns an integer index
	(from 0, & of same len as array passed in) such that, uniq_vals[idx] gives the original array passed in

	higher dimensional cross tabulations are supported (eg, 2D & 3D)

	cross tabulation on two variables (columns):

	>>> q1 = NP.array([7, 8, 8, 8, 5, 6, 4, 6, 6, 8, 4, 6, 6, 6, 6, 8, 8, 5, 8, 6])
	>>> q2 = NP.array([6, 4, 6, 4, 8, 8, 4, 8, 7, 4, 4, 8, 8, 7, 5, 4, 8, 4, 4, 4])

	>>> uv, xt = xtab(q1, q2)
	>>> uv
	(array([4, 5, 6, 7, 8]), array([4, 5, 6, 7, 8]))

	>>> xt
	array([[2, 0, 0, 0, 0],
	[1, 0, 0, 0, 1],
	[1, 1, 0, 2, 4],
	[0, 0, 1, 0, 0],
	[5, 0, 1, 0, 1]], dtype=uint64)
	'''
	if not all(len(col) == len(cols[0]) for col in cols[1:]):
	raise ValueError("all arguments must be same size")

	if len(cols) == 0:
	raise TypeError("xtab() requires at least one argument")

	fnx1 = lambda q: len(q.squeeze().shape)
	if not all([fnx1(col) == 1 for col in cols]):
	raise ValueError("all input arrays must be 1D")

	if apply_wt:
	cols, wt = cols[:-1], cols[-1]
	else:
	wt = 1

	uniq_vals_all_cols, idx = zip( *(NP.unique(col, return_inverse=True) for col in cols) )
	shape_xt = [uniq_vals_col.size for uniq_vals_col in uniq_vals_all_cols]
	dtype_xt = 'float' if apply_wt else 'uint'
	xt = NP.zeros(shape_xt, dtype=dtype_xt)
	NP.add.at(xt, idx, wt)
	return uniq_vals_all_cols, xt