Open shoyer opened 6 years ago
Since https://github.com/pydata/xarray/issues/3206 has been implemented now:
Maybe fancy boolean indexing (da[boolean_mask]
) could return a sparse array as well.
Just wanted to confirm, that boolean indexing is indeed highly relevant, especially for assigning values instead of just selecting them. Here is a use case which I encounter very often:
I'm working with very sparse data (e.g a satellite image of some islands surrounded by water), and I want to modify it using some_vectorized_function()
. Of course I could use some_vectorized_function()
to process the whole image, but boolean masking allows me to save a lot of computations.
Here is how I would achieve this in numpy:
import numpy as np
import some_vectorized_function
image = np.array( # image.shape == (3, 7, 7)
[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 454, 454, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 565, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 343, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 454, 565, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 667, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 878, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 565, 676, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 323, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 545, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]]
)
image = np.moveaxis(image, 0, -1) # image.shape == (7, 7, 3)
# "image" is a standard RGB image
# with shape == (height, width, channel)
# but only 4 pixels contain relevant data!
mask = np.all(image > 0, axis=-1) # mask.shape == (7, 7)
# mask.dtype == bool
# mask.sum() == 4
image[mask] = some_vectorized_function(image[mask]) # len(image[mask]) == 4
# image[mask].shape == (4, 3)
The most important fact here is that image[mask]
is just a list of 4 pixels, which I can process and then assign them back into their original place. And as you see, this boolean masking also plays very nice with broadcasting, which allows me to mask a 3D array with a 2D mask.
Unfortunately, nothing like this is currently possible with XArray. If implemented, it would enable some crazy speedups for operations like spatial interpolation, where we don't want to interpolate the whole image, but only some pixels that we care about.
I've added the "good first issue" label — at least the first two bullets of the proposal would be relatively simple to implement, given they're mostly syntactic sugar.
It's worth noting that there is at least one other way boolean indexing could work:
ds[key]
could work like ds.stack({key.name: key.dims}).isel({key.name: np.flatnonzero(key.data)})
, except without creating a MultiIndex. Arguably this might be more useful and also more consistent with NumPy itself. It's also more similar to the operation @Hoeze wants in https://github.com/pydata/xarray/issues/5179.We can't support both with the same syntax, so we have to make a choice here :).
See also the discussion about what drop_duplicates
/unique
should do over in https://github.com/pydata/xarray/pull/5089.
I've been trying to conceptualize why I think the where
equivalence (the original proposal) is better than the stack
proposal (the latter). I think it's mostly:
da[key] = value
?stack
result, it's less work to do original -> where
result -> stack
result relative to original -> stack
result -> where
result; which suggests they're more composable?But I don't do much pointwise indexing — and so maybe we do want to prioritize that
I've been trying to conceptualize why I think the
where
equivalence (the original proposal) is better than thestack
proposal (the latter).
Here are two reasons why I like the stack
version:
As a side note: one nice feature of using isel()
for stacking is that it does not create a MultiIndex, which can be expensive. But there's no reason why we necessarily need to do that for stack()
. I'll open a new issue to discuss adding an optional parameter.
- I'm not sure how the setitem would work;
da[key] = value
?
To match the semantics of NumPy, value
would need to have matching dims/coords to those of da[key]
. In other words, it would also need to be stacked.
- If someone wants the
stack
result, it's less work to do original ->where
result ->stack
result relative to original ->stack
result ->where
result; which suggests they're more composable?
I'm not quite sure this is true -- it's the difference between needing to call stack()
vs unstack()
.
OK great. To confirm, this is what it would look like:
Context:
In [81]: da = xr.DataArray(np.arange(12).reshape(3,4), dims=list('ab'))
In [82]: da
Out[82]:
<xarray.DataArray (a: 3, b: 4)>
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
Dimensions without coordinates: a, b
In [84]: key = da % 3 == 0
In [83]: key
Out[83]:
<xarray.DataArray (a: 3, b: 4)>
array([[ True, False, False, True],
[False, False, True, False],
[False, True, False, False]])
Dimensions without coordinates: a, b
Currently
In [85]: da[key]
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-85-7fd83c907cb6> in <module>
----> 1 da[key]
...
~/.asdf/installs/python/3.8.8/lib/python3.8/site-packages/xarray/core/variable.py in _validate_indexers(self, key)
697 )
698 if k.ndim > 1:
--> 699 raise IndexError(
700 "{}-dimensional boolean indexing is "
701 "not supported. ".format(k.ndim)
IndexError: 2-dimensional boolean indexing is not supported.
Current proposal ("stack
"), of da[key]
and with a dimension of key
's name (and probably no multiindex):
In [86]: da.values[key.values]
Out[86]: array([0, 3, 6, 9]) # But the xarray version
Previous suggestion ("where
"), for the result of da[key]
:
In [87]: da.where(key)
Out[87]:
<xarray.DataArray (a: 3, b: 4)>
array([[ 0., nan, nan, 3.],
[nan, nan, 6., nan],
[nan, 9., nan, nan]])
Dimensions without coordinates: a, b
(small follow up I'll put in another message, for clarity)
I'm not quite sure this is true -- it's the difference between needing to call
stack()
vsunstack()
.
This was a tiny point so it's fine to discard. I had meant that producing the where
result via the stack
result requires a stack
and unstack
. But producing the stack
result via a where
result requires only one stack
— the where
result is very cheap.
OK great. To confirm, this is what it would look like:
Yes, this looks right to me.
Current proposal ("
stack
"), ofda[key]
and with a dimension ofkey
's name (and probably no multiindex):In [86]: da.values[key.values] Out[86]: array([0, 3, 6, 9]) # But the xarray version
The part about this new proposal that is most annoying is that the key
needs a name
, which we can use to name the new dimension. That's not too hard to do, but it is little annoying -- in practice you would have to write something like da[key.rename('key_name')]
much of the time to make this work.
I'm still working through this. Using this to jot down my notes, no need to respond.
One property that seems to be lacking is that if key
changes from n-1
to n
dimensions, the behavior changes (also outlined here):
In [171]: a
Out[171]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
In [172]: mask
Out[172]: array([ True, False, True])
In [173]: a[mask]
Out[173]:
array([[ 0, 1, 2, 3],
[ 8, 9, 10, 11]])
...as expected, but now let's make a 2D mask...
In [174]: full_mask = np.broadcast_to(mask[:, np.newaxis], (3,4))
In [175]: full_mask
Out[175]:
array([[ True, True, True, True],
[False, False, False, False],
[ True, True, True, True]])
In [176]: a[full_mask]
Out[176]: array([ 0, 1, 2, 3, 8, 9, 10, 11]) # flattened!
Current proposal ("
stack
"), ofda[key]
and with a dimension ofkey
's name (and probably no multiindex):In [86]: da.values[key.values] Out[86]: array([0, 3, 6, 9]) # But the xarray version
The part about this new proposal that is most annoying is that the
key
needs aname
, which we can use to name the new dimension. That's not too hard to do, but it is little annoying -- in practice you would have to write something likeda[key.rename('key_name')]
much of the time to make this work.
IMO, the perfect solution would be masking support.
I.e. da[key]
would return the same array with an additional variable da.mask == key
:
In [87]: da[key]
Out[87]:
<xarray.DataArray (a: 3, b: 4)>
array([[ 0, <NA>, <NA>, 3],
[<NA>, <NA>, 6, <NA>],
[<NA>, 9, <NA>, <NA>]])
dtype: int
Dimensions without coordinates: a, b
Then we could have something like da[key].stack(new_dim=["a", "b"], dropna=True)
:
In [87]: da[key].stack(new_dim=["a", "b"], dropna=True)
Out[87]:
<xarray.DataArray (newdim: 4)>
array([0, 3, 6, 9])
coords{
"a" (newdim): [0, 0, 1, 2],
"b" (newdim): [0, 3, 2, 1],
}
Dimensions without coordinates: newdim
Here, dropna=True
would allow avoiding to create the cross-product of a, b
.
Also, that would avoid all those unnecessary float
casts for free.
stack(new_dim=["a", "b"], dropna=True)
This could be useful (potentially we can open a different issue). While someone can call .dropna
, that coerces to floats (or some type that supports missing) and can allocate more than is needed. Potentially this can be considered along with issues around sparse, e.g. https://github.com/pydata/xarray/issues/3245, https://github.com/pydata/xarray/issues/4143
Originally from https://github.com/pydata/xarray/issues/974
For boolean indexing:
da[key]
wherekey
is a boolean labelled array (with any number of dimensions) is made equivalent toda.where(key.reindex_like(ds), drop=True)
. This matches the existing behavior ifkey
is a 1D boolean array. For multi-dimensional arrays, even though the result is now multi-dimensional, this coupled with automatic skipping of NaNs means thatda[key].mean()
gives the same result as in NumPy.da[key] = value
wherekey
is a boolean labelled array can be made equivalent toda = da.where(*align(key.reindex_like(da), value.reindex_like(da)))
(that is, the three argument form ofwhere
).da[key_0, ..., key_n]
where all ofkey_i
are boolean arrays gets handled in the usual way. It is anIndexingError
to supply multiple labelled keys if any of them are not already aligned with as the corresponding index coordinates (and share the same dimension name). If they want alignment, we suggest users simply writeda[key_0 & ... & key_n]
.