How to filter arrays: cutting vs. masking#

import awkward as ak
import numpy as np

The problem with slicing#

When you write a mathematical formula using binary operators like + and *, or NumPy universal functions (ufuncs) like np.sqrt, the shapes of nested lists must align. If the arrays in an expression were derived from a single array, this is often automatic. For instance,

original_array = ak.Array([
    [
        {"title": "zero", "x": 0, "y": 0},
        {"title": "one", "x": 1, "y": 1.1},
        {"title": "two", "x": 2, "y": 2.2},
    ],
    [],
    [
        {"title": "three", "x": 3, "y": 3.3},
        {"title": "four", "x": 4, "y": 4.4},
    ],
    [
        {"title": "five", "x": 5, "y": 5.5},
    ],
    [
        {"title": "six", "x": 6, "y": 6.6},
        {"title": "seven", "x": 7, "y": 7.7},
        {"title": "eight", "x": 8, "y": 8.8},
        {"title": "nine", "x": 9, "y": 9.9},
    ],
])

array_x = original_array.x
array_y = original_array.y

The array_x and array_y have the same number of lists and the same numbers of items in each list because they were both slices of the original_array.

array_x

[[0, 1, 2],
 [],
 [3, 4],
 [5],
 [6, 7, 8, 9]]
---------------------
type: 5 * var * int64

array_y

[[0, 1.1, 2.2],
 [],
 [3.3, 4.4],
 [5.5],
 [6.6, 7.7, 8.8, 9.9]]
-----------------------
type: 5 * var * float64

Thus, they can be used together in a mathematical formula.

array_x**2 + array_y**2

[[0, 2.21, 8.84],
 [],
 [19.9, 35.4],
 [55.2],
 [79.6, 108, 141, 179]]
-----------------------
type: 5 * var * float64

However, if one array is sliced, or if the two arrays are sliced by different criteria, they would no longer line up:

sliced_x = array_x[array_x > 3]
sliced_y = array_y[array_y > 3]

sliced_x

[[],
 [],
 [4],
 [5],
 [6, 7, 8, 9]]
---------------------
type: 5 * var * int64

sliced_y

[[],
 [],
 [3.3, 4.4],
 [5.5],
 [6.6, 7.7, 8.8, 9.9]]
-----------------------
type: 5 * var * float64

Notice that the first was sliced with array_x > 3 and the second was sliced with array_y > 3, and as a result, the third list differs in length between the two arrays:

sliced_x[2], sliced_y[2]

(<Array [4] type='1 * int64'>, <Array [3.3, 4.4] type='2 * float64'>)

If we try to use these together, we get a ValueError:

sliced_x**2 + sliced_y**2

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[11], line 1
----> 1 sliced_x**2 + sliced_y**2

File ~/micromamba/envs/awkward-docs/lib/python3.11/site-packages/awkward/_operators.py:54, in _binary_method.<locals>.func(self, other)
if _disables_array_ufunc(other):
   return NotImplemented
---> 54 return ufunc(self, other)

File ~/micromamba/envs/awkward-docs/lib/python3.11/site-packages/awkward/highlevel.py:1594, in Array.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
name = f"{type(ufunc).__module__}.{ufunc.__name__}.{method!s}"
with ak._errors.OperationErrorContext(name, inputs, kwargs):
-> 1594     return ak._connect.numpy.array_ufunc(ufunc, method, inputs, kwargs)

File ~/micromamba/envs/awkward-docs/lib/python3.11/site-packages/awkward/_connect/numpy.py:469, in array_ufunc(ufunc, method, inputs, kwargs)
       raise TypeError(
           "no {}.{} overloads for custom types: {}".format(
               type(ufunc).__module__, ufunc.__name__, ", ".join(error_message)
           )
       )
   return None
--> 469 out = ak._broadcasting.broadcast_and_apply(
   inputs,
   action,
   depth_context=depth_context,
   lateral_context=lateral_context,
   allow_records=False,
   function_name=ufunc.__name__,
)
out_named_axis = functools.reduce(
   _unify_named_axis, lateral_context[NAMED_AXIS_KEY].named_axis
)
if len(out) == 1:

File ~/micromamba/envs/awkward-docs/lib/python3.11/site-packages/awkward/_broadcasting.py:1200, in broadcast_and_apply(inputs, action, depth_context, lateral_context, allow_records, left_broadcast, right_broadcast, numpy_to_regular, regular_to_jagged, function_name, broadcast_parameters_rule)
backend = backend_of(*inputs, coerce_to_common=False)
isscalar = []
-> 1200 out = apply_step(
   backend,
   broadcast_pack(inputs, isscalar),
   action,
   0,
   depth_context,
   lateral_context,
   {
       "allow_records": allow_records,
       "left_broadcast": left_broadcast,
       "right_broadcast": right_broadcast,
       "numpy_to_regular": numpy_to_regular,
       "regular_to_jagged": regular_to_jagged,
       "function_name": function_name,
       "broadcast_parameters_rule": broadcast_parameters_rule,
   },
)
assert isinstance(out, tuple)
return tuple(broadcast_unpack(x, isscalar) for x in out)

File ~/micromamba/envs/awkward-docs/lib/python3.11/site-packages/awkward/_broadcasting.py:1178, in apply_step(backend, inputs, action, depth, depth_context, lateral_context, options)
   return result
elif result is None:
-> 1178     return continuation()
else:
   raise AssertionError(result)

File ~/micromamba/envs/awkward-docs/lib/python3.11/site-packages/awkward/_broadcasting.py:1147, in apply_step.<locals>.continuation()
# Any non-string list-types?
elif any(x.is_list and not is_string_like(x) for x in contents):
-> 1147     return broadcast_any_list()
# Any RecordArrays?
elif any(x.is_record for x in contents):

File ~/micromamba/envs/awkward-docs/lib/python3.11/site-packages/awkward/_broadcasting.py:671, in apply_step.<locals>.broadcast_any_list()
       nextinputs.append(x)
       nextparameters.append(NO_PARAMETERS)
--> 671 outcontent = apply_step(
   backend,
   nextinputs,
   action,
   depth + 1,
   copy.copy(depth_context),
   lateral_context,
   options,
)
assert isinstance(outcontent, tuple)
parameters = parameters_factory(nextparameters, len(outcontent))

File ~/micromamba/envs/awkward-docs/lib/python3.11/site-packages/awkward/_broadcasting.py:1178, in apply_step(backend, inputs, action, depth, depth_context, lateral_context, options)
   return result
elif result is None:
-> 1178     return continuation()
else:
   raise AssertionError(result)

File ~/micromamba/envs/awkward-docs/lib/python3.11/site-packages/awkward/_broadcasting.py:1147, in apply_step.<locals>.continuation()
# Any non-string list-types?
elif any(x.is_list and not is_string_like(x) for x in contents):
-> 1147     return broadcast_any_list()
# Any RecordArrays?
elif any(x.is_record for x in contents):

File ~/micromamba/envs/awkward-docs/lib/python3.11/site-packages/awkward/_broadcasting.py:722, in apply_step.<locals>.broadcast_any_list()
for i, ((named_axis, ndim), x, x_is_string) in enumerate(
   zip(named_axes_with_ndims, inputs, input_is_string)
):
   if isinstance(x, listtypes) and not x_is_string:
--> 722         next_content = broadcast_to_offsets_avoiding_carry(x, offsets)
       nextinputs.append(next_content)
       nextparameters.append(x._parameters)

File ~/micromamba/envs/awkward-docs/lib/python3.11/site-packages/awkward/_broadcasting.py:385, in broadcast_to_offsets_avoiding_carry(list_content, offsets)
       return list_content.content[:next_length]
   else:
--> 385         return list_content._broadcast_tooffsets64(offsets).content
elif isinstance(list_content, ListArray):
   # Is this list contiguous?
   if index_nplike.array_equal(
       list_content.starts.data[1:], list_content.stops.data[:-1]
   ):
       # Does this list match the offsets?

File ~/micromamba/envs/awkward-docs/lib/python3.11/site-packages/awkward/contents/listoffsetarray.py:412, in ListOffsetArray._broadcast_tooffsets64(self, offsets)
   next_content = self._content[this_start:]
if index_nplike.known_data and not index_nplike.array_equal(
   this_zero_offsets, offsets.data
):
--> 412     raise ValueError("cannot broadcast nested list")
return ListOffsetArray(
   offsets, next_content[: offsets[-1]], parameters=self._parameters
)

ValueError: cannot broadcast nested list

This error occurred while calling

    numpy.add.__call__(
        <Array [[], [], ..., [25], [36, 49, 64, 81]] type='5 * var * int64'>
        <Array [[], [], ..., [43.6, 59.3, 77.4, 98]] type='5 * var * float64'>
    )

Sometimes, these misalignments are overt, but sometimes they’re subtle and embedded deep within a very large array. You can start investigating a problem like this with ak.num():

ak.num(sliced_x) != ak.num(sliced_y)

[False,
 False,
 True,
 False,
 False]
--------------
type: 5 * bool

np.nonzero(ak.to_numpy(ak.num(sliced_x) != ak.num(sliced_y)))

(array([2]),)

But it’s also possible to avoid them in the first place.

Masking with missing values#

The problem was that the two arrays’ shapes changed differently; instead, we’ll slice them in such a way that their shapes don’t change at all.

The ak.mask() function uses a boolean array like a slice, but takes values that line up with False and returns None instead of removing them.

ak.mask(array_x, array_x > 3)

[[None, None, None],
 [],
 [None, 4],
 [5],
 [6, 7, 8, 9]]
----------------------
type: 5 * var * ?int64

It can also be accessed as an array property, with square brackets, so that it resembles a slice:

masked_x = array_x.mask[array_x > 3]
masked_y = array_y.mask[array_y > 3]

masked_x

[[None, None, None],
 [],
 [None, 4],
 [5],
 [6, 7, 8, 9]]
----------------------
type: 5 * var * ?int64

masked_y

[[None, None, None],
 [],
 [3.3, 4.4],
 [5.5],
 [6.6, 7.7, 8.8, 9.9]]
------------------------
type: 5 * var * ?float64

The results of these two masks can be used in a mathematical expression because they line up:

result = masked_x**2 + masked_y**2
result

[[None, None, None],
 [],
 [None, 35.4],
 [55.2],
 [79.6, 108, 141, 179]]
------------------------
type: 5 * var * ?float64

Now only one problem remains: the None (missing) values might be undesirable in the output. There are several ways to get rid of them:

ak.drop_none() eliminates None, like a slice, but it can be done once at the end of a calculation,
ak.fill_none() replaces None with a chosen value,
ak.flatten() removes list structure, and if the None values are at the level of a list (the ones in result aren’t), they’ll be removed too,
ak.singletons() replaces None with [] and any other value x with [x]. The resulting lists all have length 0 or length 1.

ak.drop_none(result, axis=1)

[[],
 [],
 [35.4],
 [55.2],
 [79.6, 108, 141, 179]]
-----------------------
type: 5 * var * float64

ak.fill_none(result, -1, axis=1)

[[-1, -1, -1],
 [],
 [-1, 35.4],
 [55.2],
 [79.6, 108, 141, 179]]
-----------------------
type: 5 * var * float64

ak.singletons(result, axis=1)

[[[], [], []],
 [],
 [[], [35.4]],
 [[55.2]],
 [[79.6], [108], [141], [179]]]
-------------------------------
type: 5 * var * var * float64

As a final note, the difference between using ak.drop_none() and slicing with the result of ak.is_none() is that ak.drop_none() also removes “missingness” from the data type; a slice does not.

result[~ak.is_none(result, axis=1)]

[[],
 [],
 [35.4],
 [55.2],
 [79.6, 108, 141, 179]]
------------------------
type: 5 * var * ?float64

(Note the ? for “option-type” before float64. This could have consequences, good or bad, at a later stage in processing.)