Can I create a dask array with a delayed shape

Question

Is it possible to create a dask array from a delayed value by specifying its shape with an other delayed value?

My algorithm won't give me the shape of the array until pretty late in the computation.

Eventually, I will be creating some blocks with shapes specified by the intermediate results of my computation, eventually calling da.concatenate on all the results (well da.block if it were more flexible)

I don't think it is too detrimental if I can't, but it would be cool if could.

Sample code

from dask import delayed
from dask import array as da
import numpy as np

n_shape = (3, 3)
shape = delayed(n_shape, nout=2)
d_shape = (delayed(n_shape[0]), delayed(n_shape[1]))
n = delayed(np.zeros)(n_shape, dtype=np.float)


# this doesn't work
# da.from_delayed(n, shape=shape, dtype=np.float)
# this doesn't work either, but I think goes a little deeper
# into the function call
da.from_delayed(n, shape=d_shape, dtype=np.float)

Answer 1

You can not provide a delayed shape, but you can state that the shape is unknown using np.nan as a value wherever you don't know a dimension

Example

import random
import numpy as np
import dask
import dask.array as da

@dask.delayed
def f():
    return np.ones((5, random.randint(10, 20)))  # a 5 x ? array

values = [f() for _ in range(5)]
arrays = [da.from_delayed(v, shape=(5, np.nan), dtype=float) for v in values]
x = da.concatenate(arrays, axis=1)

>>> x
dask.array<concatenate, shape=(5, nan), dtype=float64, chunksize=(5, nan)>

>>> x.shape
(5, np.nan)

>>> x.compute().shape
(5, 88)

Docs

See http://dask.pydata.org/en/latest/array-chunks.html#unknown-chunks