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ChunkedArrays slice differently than JaggedArrays #229

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tamasgal opened this issue Jan 24, 2020 · 4 comments
Closed

ChunkedArrays slice differently than JaggedArrays #229

tamasgal opened this issue Jan 24, 2020 · 4 comments

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@tamasgal
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I discovered this behaviour while I was processing data read by uproot, but it seems it's awkward related.

The data I read it has doubly nested lists and it seems that slicing is different on ChunkedArrays or ObjectArrays (which are returned by the lazyarray() respectively array() method of the uproot branch). However, when I pass the data into a constructor of JaggedArray, the slicing does what is "should".

Here, I try to access the first element (which is a list) of each sublist using a [:, 0] slice. The ChunkedArray returns weird values, however, if I put it into awkward.array.from_iter and slice afterwards, I get the correct values.

Here is the file I used:
dummy.root.zip

I played around with different methods but could not get it work...

In [166]: import uproot

In [167]: f = uproot.open("dummy.root")

In [168]: rs = f['E']['Evt']['trks']['trks.rec_stages'].lazyarray()

In [169]: rs
Out[169]: <ChunkedArray [[[1, 3, 5, 4], [1, 3, 5], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1]] [[1, 3, 5, 4], [1, 3, 5], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1]] [[1, 3, 5, 4], [1, 3, 5], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1]] ... [[1, 3, 5, 4], [1, 3, 5], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1]] [[1, 3, 5, 4], [1, 3, 5], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1]] [[1, 3, 5, 4], [1, 3, 5], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1, 3], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], [1]]] at 0x7fec98103b90>

In [170]: rs[:, 0]
Out[170]: <ChunkedArray [[0 0 0 ... 0 0 1] [0 0 0 ... 0 0 1] [0 0 0 ... 0 0 1] ... [0 0 0 ... 0 0 1] [0 0 0 ... 0 0 1] [0 0 0 ... 0 0 1]] at 0x7fec98107e50>

In [171]: import awkward

In [172]: awkward.fromiter(rs)
Out[172]: <JaggedArray [[[1 3 5 4] [1 3 5] [1 3] ... [1] [1] [1]] [[1 3 5 4] [1 3 5] [1 3] ... [1] [1] [1]] [[1 3 5 4] [1 3 5] [1 3] ... [1] [1] [1]] ... [[1 3 5 4] [1 3 5] [1 3] ... [1] [1] [1]] [[1 3 5 4] [1 3 5] [1 3] ... [1] [1] [1]] [[1 3 5 4] [1 3 5] [1 3] ... [1] [1] [1]]] at 0x7fec98203b10>

In [173]: awkward.fromiter(rs)[:, 0]
Out[173]: <JaggedArray [[1 3 5 4] [1 3 5 4] [1 3 5 4] ... [1 3 5 4] [1 3 5 4] [1 3 5 4]] at 0x7fec98204ad0>
@nsmith-
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nsmith- commented Jan 25, 2020 •
edited
Loading

You might try to see if .lazyarray(chunked=False) works better. VirtualArrays are more well-tested than ChunkedArrays. If you need to limit the array size, the entrystart and entrystop parameters can function to limit them.

@tamasgal
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It's the same as with .array(). Here is the full output of all three approaches:

[ins] In [8]: rs = f['E']['Evt']['trks']['trks.rec_stages'].lazyarray(chunked=Fa
         ...: lse)

[ins] In [9]: rs[:, 0]
Out[9]:
[array([0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 5, 0, 0, 0, 4, 0, 0,
        0, 3, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 5, 0, 0, 0, 2, 0, 0, 0, 1,
        0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0,
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        0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0,
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        0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0,
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        0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1], dtype=uint8),
 array([0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 5, 0, 0, 0, 4, 0, 0,
        0, 3, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 5, 0, 0, 0, 2, 0, 0, 0, 1,
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        0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3,
        0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0,
        0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1,
        0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0,
        0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2,
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        0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
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        0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
       dtype=uint8),
 array([0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 5, 0, 0, 0, 4, 0, 0,
        0, 3, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 5, 0, 0, 0, 2, 0, 0, 0, 1,
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 array([0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 5, 0, 0, 0, 4, 0, 0,
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 array([0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 5, 0, 0, 0, 4, 0, 0,
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 array([0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 5, 0, 0, 0, 4, 0, 0,
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[ins] In [10]: rs = f['E']['Evt']['trks']['trks.rec_stages'].array()

[ins] In [11]: rs[:, 0]
Out[11]:
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        0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2,
        0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0,
        0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3,
        0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0,
        0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1,
        0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0,
        0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2,
        0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0,
        0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
        0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
        0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
        0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
        0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
        0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
        0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
        0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
        0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
        0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
        0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
        0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
        0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
        0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1], dtype=uint8)]

[ins] In [12]: rs = f['E']['Evt']['trks']['trks.rec_stages'].lazyarray()

[ins] In [13]: rs[:, 0]
Out[13]: <ChunkedArray [[0 0 0 ... 0 0 1] [0 0 0 ... 0 0 1] [0 0 0 ... 0 0 1] ... [0 0 0 ... 0 0 1] [0 0 0 ... 0 0 1] [0 0 0 ... 0 0 1]] at 0x0001031d4d90>

[ins] In [14]: rs[:, 0][0]
Out[14]:
array([0, 0, 0, 4, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 5, 0, 0, 0, 4, 0, 0,
       0, 3, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 5, 0, 0, 0, 2, 0, 0, 0, 1,
       0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0,
       0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2,
       0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0,
       0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3,
       0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0,
       0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1,
       0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0,
       0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2,
       0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0,
       0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
       0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
       0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
       0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
       0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
       0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
       0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,
       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1], dtype=uint8)

@tamasgal
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Any ideas? I guess you are very busy with awkward 1.0 so maybe it’s even worth to wait for that and its integration into uproot. Not sure about the timeline though.

@jpivarski
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I just got a chance to triage this today. I had been worried that it might be a bug in the ChunkedArray you get from lazyarrays (as @nsmith- can attest, this one has caused us a lot of trouble), but actually it's a misunderstanding of the ObjectArrays you get from arrays. You're not the first, and ObjectArrays are too confusing to be allowed to live—they're being removed from Awkward 1.0.

To start with, note that array and lazyarray give the same meaning of the data, at the level of lists:

import uproot
branch = uproot.open("issue229.root")["E"]["trks.rec_stages"]
fromarray = branch.array()[:, 0]
fromlazyarray = branch.lazyarray()[:, 0]
[x.tolist() for x in fromarray] == fromlazyarray.tolist()
# returns True

and also

import awkward
awkward.fromiter(fromarray)
awkward.fromiter(fromarray).tolist() == fromlazyarray.tolist()
# returns True

You only get a different answer if you eliminate the effect of ObjectArrays with awkward.fromiter:

fromawkward = awkward.fromiter(branch.array())
fromawkward[:, 0].tolist() == fromlazyarray.tolist()
# returns False!

The key point is that deep slices (slices that are tuples, like (slice(None), 0)) don't cross from non-ObjectArrays into ObjectArrays. Perhaps what you're reporting is an error; perhaps it should have raised an exception if you even try to. Your ObjectArray is a one-dimensional array of Python lists; each Python list is generated on demand (deserializing the unsplit ROOT data), and the ObjectArray doesn't know that they're all going to be lists with the same depth.

NumPy's object arrays have the same issue: if dtype is "O", then a slice like (slice(None), 0) couldn't be applied to this one-dimensional array of Python lists (although it nicely raises an IndexError).

numpy.array(branch.array())[:, 0]
# Traceback (most recent call last):
#   File "<stdin>", line 1, in <module>
# IndexError: too many indices for array

I made a wrong design choice by introducing ObjectArray: it delays evaluation of the (expensive) unsplit-list deserialization by introducing an opaque type that doesn't work like any other data. In Awkward 1.0, the unsplit-list deserialization will be over an order of magnitude faster (no longer the very noticeable performance hit) as shown on page 14/16 of this talk, and the Methods and ObjectArray mechanisms will be replaced by a single non-opaque mechanism as shown in this section of the Coffea demo.

So, not a bug, but exactly the sort of confusing feature that will be eliminated by transitioning to Awkward 1.0.

@tamasgal tamasgal mentioned this issue Mar 4, 2020
Closed
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@tamasgal @jpivarski @nsmith-