def setxor1d(pda1: pdarray,
pda2: pdarray,
assume_unique: bool = False) -> pdarray:
"""
Find the set exclusive-or (symmetric difference) of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
pda1 : pdarray
Input array.
pda2 : pdarray
Input array.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
pdarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Raises
------
TypeError
Raised if either pda1 or pda2 is not a pdarray
RuntimeError
Raised if the dtype of either pdarray is not supported
Notes
-----
ak.setxor1d is not supported for bool or float64 pdarrays
Examples
--------
>>> a = ak.array([1, 2, 3, 2, 4])
>>> b = ak.array([2, 3, 5, 7, 5])
>>> ak.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if pda1.size == 0:
return pda2 # return other pdarray if pda1 is empty
if pda2.size == 0:
return pda1 # return other pdarray if pda2 is empty
if pda1.dtype == int and pda2.dtype == int:
repMsg = generic_msg(cmd="setxor1d", args="{} {} {}".\
format(pda1.name, pda2.name, assume_unique))
return create_pdarray(cast(str, repMsg))
if not assume_unique:
pda1 = cast(pdarray, unique(pda1))
pda2 = cast(pdarray, unique(pda2))
aux = concatenate((pda1, pda2), ordered=False)
aux_sort_indices = argsort(aux)
aux = aux[aux_sort_indices]
flag = concatenate((array([True]), aux[1:] != aux[:-1], array([True])))
return aux[flag[1:] & flag[:-1]]
def setxor1d(pda1, pda2, assume_unique=False):
"""
Find the set exclusive-or (symmetric difference) of two arrays.
Return the sorted, unique values that are in only one (not both) of the
input arrays.
Parameters
----------
pda1 : pdarray
Input array.
pda2 : pdarray
Input array.
assume_unique : bool
If True, the input arrays are both assumed to be unique, which
can speed up the calculation. Default is False.
Returns
-------
pdarray
Sorted 1D array of unique values that are in only one of the input
arrays.
Examples
--------
>>> a = ak.array([1, 2, 3, 2, 4])
>>> b = ak.array([2, 3, 5, 7, 5])
>>> ak.setxor1d(a,b)
array([1, 4, 5, 7])
"""
if isinstance(pda1, pdarray) and isinstance(pda2, pdarray):
if pda1.size == 0:
return pda2 # return other pdarray if pda1 is empty
if pda2.size == 0:
return pda1 # return other pdarray if pda2 is empty
if pda1.dtype == int and pda2.dtype == int:
repMsg = generic_msg("setxor1d {} {} {}".format(pda1.name, pda2.name, assume_unique))
return create_pdarray(repMsg)
if not assume_unique:
pda1 = unique(pda1)
pda2 = unique(pda2)
aux = concatenate((pda1, pda2))
aux_sort_indices = argsort(aux)
aux = aux[aux_sort_indices]
flag = concatenate((array([True]), aux[1:] != aux[:-1], array([True])))
return aux[flag[1:] & flag[:-1]]
else:
raise TypeError("must be pdarray {} or {}".format(pda1,pda2))
def broadcast(self, values : pdarray) -> pdarray:
"""
Fill each group's segment with a constant value.
Parameters
----------
values : pdarray
The values to put in each group's segment
Returns
-------
pdarray
The broadcast values
Raises
------
TypeError
Raised if value is not a pdarray object
ValueError
Raised if the values array does not have one
value per segment
Notes
-----
This function is a sparse analog of ``np.broadcast``. If a
GroupBy object represents a sparse matrix (tensor), then
this function takes a (dense) column vector and replicates
each value to the non-zero elements in the corresponding row.
The returned array is in permuted (grouped) order. To get
back to the order of the array on which GroupBy was called,
the user must invert the permutation (see below).
Examples
--------
>>> a = ak.array([0, 1, 0, 1, 0])
>>> values = ak.array([3, 5])
>>> g = ak.GroupBy(a)
# Result is in grouped order
>>> g.broadcast(values)
array([3, 3, 3, 5, 5]
>>> b = ak.zeros_like(a)
# Result is in original order
>>> b[g.permutation] = g.broadcast(values)
>>> b
array([3, 5, 3, 5, 3])
"""
if not isinstance(values, pdarray):
raise TypeError("Vals must be pdarray")
if values.size != self.segments.size:
raise ValueError("Must have one value per segment")
temp = zeros(self.size, values.dtype)
if values.size == 0:
return temp
diffs = concatenate((array([values[0]]), values[1:] - values[:-1]))
temp[self.segments] = diffs
return cumsum(temp)
def broadcast(self, values : pdarray) -> pdarray:
"""
Fill each group's segment with a constant value.
Parameters
----------
values : pdarray
The values to put in each group's segment
Returns
-------
pdarray
The broadcast values
Raises
------
TypeError
Raised if value is not a pdarray object
ValueError
Raised if the values array does not have one
value per segment
Notes
-----
This function is a sparse analog of ``np.broadcast``. If a
GroupBy object represents a sparse matrix (tensor), then
this function takes a (dense) column vector and replicates
each value to the non-zero elements in the corresponding row.
The returned array is in permuted (grouped) order. To get
back to the order of the array on which GroupBy was called,
the user must invert the permutation (see below).
Examples
--------
>>> a = ak.array([0, 1, 0, 1, 0])
>>> values = ak.array([3, 5])
>>> g = ak.GroupBy(a)
# Result is in grouped order
>>> g.broadcast(values)
array([3, 3, 3, 5, 5]
>>> b = ak.zeros_like(a)
# Result is in original order
>>> b[g.permutation] = g.broadcast(values)
>>> b
array([3, 5, 3, 5, 3])
>>> a = ak.randint(1,5,10)
>>> a
array([3, 1, 4, 4, 4, 1, 3, 3, 2, 2])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.count()
>>> g.broadcast(counts > 2)
array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
>>> g.broadcast(counts == 3)
array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
>>> g.broadcast(counts < 4)
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
"""
'''if values a boolean array, convert to an int64 array, which
is needed for now because Arkouda does not support broadcasting
of boolean arrays'''
if values.dtype == np.bool:
values = 1*values
if values.size != self.segments.size:
raise ValueError("Must have one value per segment")
temp = zeros(self.size, values.dtype)
if values.size == 0:
return temp
diffs = concatenate((array([values[0]]), values[1:] - values[:-1]))
temp[self.segments] = diffs
return cumsum(temp)
