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)