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  • Sep 15 08:05
    stuartarchibald commented #126
  • Sep 11 13:25

    hameerabbasi on master

    Remove extra debugging line int… (compare)

  • Sep 10 10:57
    hameerabbasi commented #406
  • Sep 10 07:43

    hameerabbasi on master

    Fix a number of random bugs. (#… (compare)

  • Sep 10 07:43
    hameerabbasi closed #407
  • Sep 10 07:43
    hameerabbasi closed #406
  • Sep 10 06:53
    codecov[bot] commented #407
  • Sep 10 06:53
    codecov[bot] commented #407
  • Sep 10 06:50
    hameerabbasi synchronize #407
  • Sep 10 06:28
    hameerabbasi closed #408
  • Sep 10 06:28
    hameerabbasi commented #408
  • Sep 10 06:27
    hameerabbasi synchronize #407
  • Sep 10 05:29

    hameerabbasi on master

    add _prune (#409) add _prune … (compare)

  • Sep 10 05:29
    hameerabbasi closed #409
  • Sep 09 22:33
    codecov[bot] commented #409
  • Sep 09 22:33
    codecov[bot] commented #409
  • Sep 09 22:30
    daletovar synchronize #409
  • Sep 09 22:29
    daletovar opened #409
  • Sep 09 16:15
    hameerabbasi edited #407
  • Sep 09 16:14
    hameerabbasi labeled #408
Alex Williams
@ahwillia
I'm writing up a paper now where I plan to cite this repo -- any suggestions on how to format that citation?
Matthew Rocklin
@mrocklin
I think that @hameerabbasi is writing up a paper for the SciPy 2018 conference. Maybe pre-emptively cite that? scipy-conference/scipy_proceedings#388
You might want to mention this on that github PR though just to bring things to his attention. I'm not sure how often he checks gitter
Alex Williams
@ahwillia
:+1: That should work great - thanks!
Hameer Abbasi
@hameerabbasi
Hi. I'm on Gitter very little, but I will try to reply sooner or later.
Hameer Abbasi
@hameerabbasi
Yes, the SciPy papers is a safe bet. There might be more later, but that's the best one to use right now.
Hameer Abbasi
@hameerabbasi
@ahwillia My paper has officially been accepted. You can find it at http://conference.scipy.org/proceedings/scipy2018/hameer_abbasi.html
Hameer Abbasi
@hameerabbasi
Please note that the link to the video will go up after I present at the conference.
Hameer Abbasi
@hameerabbasi
-sameroom portal
Sameroom
@sameroom-bot
<Sameroom> Your Portal URL is https://sameroom.io/kIdMLCSI -- you can send the URL to someone on a different team to share this room. Note: you can connect more than two teams this way.
<Sameroom> I've connected 1 new room #sparse (Quansight) on Slack. See map
[Hameer Abbasi, Quansight] I was having trouble coming up with GSoC ideas for PyData/Sparse.
[Hameer Abbasi, Quansight] They all seem fairly involved.
[Hameer Abbasi, Quansight] Anyone have any decent ideas?
Sameroom
@sameroom-bot

[Unknown, Quansight]
Pull request opened by eric-wieser
pydata/sparse#307
Over in pygae/clifford, we're finding we want to be able to write optimized loops using arr.coords and arr.data.

This aims to be the minimum amount needed to make that work.

Marking as draft since this has no tests, and @lower_constant isn't working for me (cc @stuartarchibald)

Sameroom
@sameroom-bot
[Unknown, Quansight]
Pull request ready for review by eric-wieser
pydata/sparse#307
Sameroom
@sameroom-bot
[Unknown, Quansight]
Issue opened by gongliyu
pydata/sparse#308
scipy.sparse array support np.shape, but sparse.COO does not.
In [1]: import sparse                                                           

In [2]: import numpy as np                                                      

In [3]: import scipy.sparse                                                     

In [4]: x = sparse.random((3, 4, 5))                                            

In [5]: x.shape                                                                 
Out[5]: (3, 4, 5)

In [6]: np.shape(x)                                                             
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-6-b17f5597101a> in <module>
----> 1 np.shape(x)

<__array_function__ internals> in shape(*args, **kwargs)

TypeError: no implementation found for 'numpy.shape' on types that implement __array_function__: [<class 'sparse._coo.core.COO'>]

In [7]: y = scipy.sparse.random(3, 4)                                           

In [8]: np.shape(y)                                                             
Out[8]: (3, 4)
Sameroom
@sameroom-bot
[Unknown, Quansight]
Issue closed by hameerabbasi
pydata/sparse#308
[Unknown, Quansight]
*1 new commit pushed to master*
pydata/sparse@b4bf66d - Fix attribute access for __array_function__. (#309)
[Unknown, Quansight]
Pull request merged by hameerabbasi
pydata/sparse#309
Sameroom
@sameroom-bot

[Unknown, Quansight]
Issue opened by hameerabbasi
pydata/sparse#310
I was considering using cppyy as the C++/Python wrapper as it allows creating runtime bindings. Everything else that can wrap TACO (pydata/sparse#299) practically can't create runtime types, and for Numba a re-implementation of all necessary parts would be needed.

For background, the reason we need runtime types is a type explosion within TACO.

Sameroom
@sameroom-bot
[Unknown, Quansight]
*1 new commit pushed to master*
pydata/sparse@40138e0 - ENH: Add numba extension support for COO objects (#307)
[Unknown, Quansight]
Pull request merged by hameerabbasi
pydata/sparse#307
Sameroom
@sameroom-bot
[Unknown, Quansight]
Issue opened by eric-wieser
pydata/sparse#311
It's been a while since the last release, might be nice to make another one.
Sameroom
@sameroom-bot
[Unknown, Quansight]
Pull request opened by hameerabbasi
pydata/sparse#312
Sameroom
@sameroom-bot
[Unknown, Quansight]
*1 new commit pushed to master*
pydata/sparse@05e981c - Add changelog for 0.9.0. (#312)
[Unknown, Quansight]
Pull request merged by hameerabbasi
pydata/sparse#312
Sameroom
@sameroom-bot
[Unknown, Quansight]
Pull request opened by hameerabbasi
pydata/sparse#313
[Unknown, Quansight]
*1 new commit pushed to master*
pydata/sparse@6a02779 - Add changelog for 0.9.1. (#313)
[Unknown, Quansight]
Pull request merged by hameerabbasi
pydata/sparse#313
Sameroom
@sameroom-bot
[Unknown, Quansight]
Issue closed by hameerabbasi
pydata/sparse#311
[Hameer Abbasi, Quansight] sparse version 0.9.1 is up!
Sameroom
@sameroom-bot

[Unknown, Quansight]
Issue opened by ezdac
pydata/sparse#314
I initialize a DOK-array with specific dtype,
don't set any value on it and convert it to a COO:

import numpy
import sparse

dok = sparse.DOK(1024, dtype=numpy.uint8)

coo = sparse.COO(dok)

The dtype of the DOK doesn't get applied to the COO.

dok.dtype
>>> dtype('uint8')
coo.dtype
>>> dtype('float64')

This is only the case when I set a nonzero value on the DOK beforehand:

dok = sparse.DOK(1024, dtype=numpy.uint8)
dok[0] = 1
coo = sparse.COO(dok)

coo.dtype
>>> dtype('uint8')

coo.dtype == dok.dtype
>>> True

You might argue that instead I should use sparse.zeros() in the first place,
and while I generally agree, I think that the above behaviour is unexpected to me and should probably be changed.

Sameroom
@sameroom-bot
[Unknown, Quansight]
Pull request opened by hameerabbasi
pydata/sparse#315
Fixes pydata/sparse#314.
Sameroom
@sameroom-bot
[Unknown, Quansight]
Issue closed by hameerabbasi
pydata/sparse#314
[Unknown, Quansight]
*1 new commit pushed to master*
pydata/sparse@77791e8 - Fix incorrect dtype on empty DOK->COO. (#315)
[Unknown, Quansight]
Pull request merged by hameerabbasi
pydata/sparse#315
Sameroom
@sameroom-bot
[Unknown, Quansight]
Issue opened by crumleyc
pydata/sparse#316
I am trying to compute the outer product between a dask array (a) and a COO sparse array (b) by calling da.outer(a,b). It looks like it is using the correct protocol, I am just wondering if there is a better way to go about this?
Sameroom
@sameroom-bot
[Unknown, Quansight]
Pull request opened by hameerabbasi
pydata/sparse#317
Sameroom
@sameroom-bot
[Unknown, Quansight]
Issue closed by hameerabbasi
pydata/sparse#316
[Unknown, Quansight]
*1 new commit pushed to master*
pydata/sparse@9dc40e1 - Add flatten method and outer function. (#317)
[Unknown, Quansight]
Pull request merged by hameerabbasi
pydata/sparse#317
Hameer Abbasi
@hameerabbasi
@/all PyData/Sparse 0.10.0 has been released!