Dask equivalent of numpy (convolve + hstack)?
I currently have a function that computes a sliding sum across a 1-D numpy array (vector) using convolve and hstack. I would like to create an equivalent function using dask, but the various ways I've tried so far have not worked out.
What I'm trying to do is to compute a "sliding sum" of n numbers of an array, unless any of the numbers are NaN in which case the sum should also be NaN. The (n - 1) elements of the result should also be NaN, since no wrap around/reach behind is assumed.
For example:
input vector: [3, 4, 6, 2, 1, 3, 5, np.NaN, 8, 5, 6]
n: 3
result: [NaN, NaN, 13, 12, 9, 6, 9, NaN, NaN, NaN, 19]
or
input vector: [1, 5, 7, 2, 3, 4, 9, 6, 3, 8]
n: 4
result: [NaN, NaN, NaN, 15, 17, 16, 18, 22, 22, 26]
The function I currently have for this using numpy functions:
def sum_to_scale(values, scale):
# don't bother if the number of values to sum is 1 (will result in duplicate array)
if scale == 1:
return values
# get the valid sliding summations with 1D convolution
sliding_sums = np.convolve(values, np.ones(scale), mode="valid")
# pad the first (n - 1) elements of the array with NaN values
return np.hstack(([np.NaN] * (scale - 1), sliding_sums))
How can I do the above using the dask array API (and/or dask_image.ndfilters) to achieve the same functionality?
Thanks in advance for any suggestions or insight.
dask
asked Nov 14 '18 at 3:31
James AdamsJames Adams
3,335125285
add a comment |
I currently have a function that computes a sliding sum across a 1-D numpy array (vector) using convolve and hstack. I would like to create an equivalent function using dask, but the various ways I've tried so far have not worked out.
What I'm trying to do is to compute a "sliding sum" of n numbers of an array, unless any of the numbers are NaN in which case the sum should also be NaN. The (n - 1) elements of the result should also be NaN, since no wrap around/reach behind is assumed.
For example:
input vector: [3, 4, 6, 2, 1, 3, 5, np.NaN, 8, 5, 6]
n: 3
result: [NaN, NaN, 13, 12, 9, 6, 9, NaN, NaN, NaN, 19]
or
input vector: [1, 5, 7, 2, 3, 4, 9, 6, 3, 8]
n: 4
result: [NaN, NaN, NaN, 15, 17, 16, 18, 22, 22, 26]
The function I currently have for this using numpy functions:
def sum_to_scale(values, scale):
# don't bother if the number of values to sum is 1 (will result in duplicate array)
if scale == 1:
return values
# get the valid sliding summations with 1D convolution
sliding_sums = np.convolve(values, np.ones(scale), mode="valid")
# pad the first (n - 1) elements of the array with NaN values
return np.hstack(([np.NaN] * (scale - 1), sliding_sums))
How can I do the above using the dask array API (and/or dask_image.ndfilters) to achieve the same functionality?
Thanks in advance for any suggestions or insight.
dask
asked Nov 14 '18 at 3:31
James AdamsJames Adams
3,335125285
add a comment |
I currently have a function that computes a sliding sum across a 1-D numpy array (vector) using convolve and hstack. I would like to create an equivalent function using dask, but the various ways I've tried so far have not worked out.
What I'm trying to do is to compute a "sliding sum" of n numbers of an array, unless any of the numbers are NaN in which case the sum should also be NaN. The (n - 1) elements of the result should also be NaN, since no wrap around/reach behind is assumed.
For example:
input vector: [3, 4, 6, 2, 1, 3, 5, np.NaN, 8, 5, 6]
n: 3
result: [NaN, NaN, 13, 12, 9, 6, 9, NaN, NaN, NaN, 19]
or
input vector: [1, 5, 7, 2, 3, 4, 9, 6, 3, 8]
n: 4
result: [NaN, NaN, NaN, 15, 17, 16, 18, 22, 22, 26]
The function I currently have for this using numpy functions:
def sum_to_scale(values, scale):
# don't bother if the number of values to sum is 1 (will result in duplicate array)
if scale == 1:
return values
# get the valid sliding summations with 1D convolution
sliding_sums = np.convolve(values, np.ones(scale), mode="valid")
# pad the first (n - 1) elements of the array with NaN values
return np.hstack(([np.NaN] * (scale - 1), sliding_sums))
How can I do the above using the dask array API (and/or dask_image.ndfilters) to achieve the same functionality?
Thanks in advance for any suggestions or insight.
dask
asked Nov 14 '18 at 3:31
James AdamsJames Adams
3,335125285
I currently have a function that computes a sliding sum across a 1-D numpy array (vector) using convolve and hstack. I would like to create an equivalent function using dask, but the various ways I've tried so far have not worked out.
What I'm trying to do is to compute a "sliding sum" of n numbers of an array, unless any of the numbers are NaN in which case the sum should also be NaN. The (n - 1) elements of the result should also be NaN, since no wrap around/reach behind is assumed.
For example:
input vector: [3, 4, 6, 2, 1, 3, 5, np.NaN, 8, 5, 6]
n: 3
result: [NaN, NaN, 13, 12, 9, 6, 9, NaN, NaN, NaN, 19]
or
input vector: [1, 5, 7, 2, 3, 4, 9, 6, 3, 8]
n: 4
result: [NaN, NaN, NaN, 15, 17, 16, 18, 22, 22, 26]
The function I currently have for this using numpy functions:
def sum_to_scale(values, scale):
# don't bother if the number of values to sum is 1 (will result in duplicate array)
if scale == 1:
return values
# get the valid sliding summations with 1D convolution
sliding_sums = np.convolve(values, np.ones(scale), mode="valid")
# pad the first (n - 1) elements of the array with NaN values
return np.hstack(([np.NaN] * (scale - 1), sliding_sums))
How can I do the above using the dask array API (and/or dask_image.ndfilters) to achieve the same functionality?
Thanks in advance for any suggestions or insight.
dask
dask
asked Nov 14 '18 at 3:31
James AdamsJames Adams
3,335125285
asked Nov 14 '18 at 3:31
James AdamsJames Adams
3,335125285
edited Nov 14 '18 at 3:41
James Adams
asked Nov 14 '18 at 3:31
James AdamsJames Adams
3,335125285
asked Nov 14 '18 at 3:31
James AdamsJames Adams
3,335125285
asked Nov 14 '18 at 3:31
James AdamsJames Adams
3,335125285
3,335125285
add a comment |
add a comment |
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