never allocate an output of numpy.ufunc












2















This question has info on using an input as an output to compute something in place with a numpy.ufunc:




  • Numpy passing input array as `out` argument to ufunc


Is it possible to avoid allocating space for an unwanted output of a numpy.ufunc? For example, say I only want one of the two outputs from modf. Can I ensure that the other, unwanted array is never allocated at all?



I thought passing _ to out might do it, but it throws an error:



import numpy as np

ar = np.arange(6)/3

np.modf(ar, out=(ar, _))    



TypeError: return arrays must be of ArrayType


As it says in the docs, passing None means that the output array is allocated in the function and returned. I can ignore the returned values, but it still has to be allocated and populated inside the function.






python numpy







share|improve this question


















  • 1





    Why worry about this? There aren't many ufunc that return more than one array. How about using np.remainder(arr, 1) instead?

    – hpaulj
    Nov 15 '18 at 23:21













  • This was more of a question about the limits of ufunc than a particular use case. My additional reading is making me pretty sure it cannot be done.

    – Paul T.
    Nov 16 '18 at 0:29


















2















This question has info on using an input as an output to compute something in place with a numpy.ufunc:




  • Numpy passing input array as `out` argument to ufunc


Is it possible to avoid allocating space for an unwanted output of a numpy.ufunc? For example, say I only want one of the two outputs from modf. Can I ensure that the other, unwanted array is never allocated at all?



I thought passing _ to out might do it, but it throws an error:



import numpy as np

ar = np.arange(6)/3

np.modf(ar, out=(ar, _))    



TypeError: return arrays must be of ArrayType


As it says in the docs, passing None means that the output array is allocated in the function and returned. I can ignore the returned values, but it still has to be allocated and populated inside the function.






python numpy







share|improve this question


















  • 1





    Why worry about this? There aren't many ufunc that return more than one array. How about using np.remainder(arr, 1) instead?

    – hpaulj
    Nov 15 '18 at 23:21













  • This was more of a question about the limits of ufunc than a particular use case. My additional reading is making me pretty sure it cannot be done.

    – Paul T.
    Nov 16 '18 at 0:29
















2












2








2


1






This question has info on using an input as an output to compute something in place with a numpy.ufunc:




  • Numpy passing input array as `out` argument to ufunc


Is it possible to avoid allocating space for an unwanted output of a numpy.ufunc? For example, say I only want one of the two outputs from modf. Can I ensure that the other, unwanted array is never allocated at all?



I thought passing _ to out might do it, but it throws an error:



import numpy as np

ar = np.arange(6)/3

np.modf(ar, out=(ar, _))    



TypeError: return arrays must be of ArrayType


As it says in the docs, passing None means that the output array is allocated in the function and returned. I can ignore the returned values, but it still has to be allocated and populated inside the function.






python numpy







share|improve this question














This question has info on using an input as an output to compute something in place with a numpy.ufunc:




  • Numpy passing input array as `out` argument to ufunc


Is it possible to avoid allocating space for an unwanted output of a numpy.ufunc? For example, say I only want one of the two outputs from modf. Can I ensure that the other, unwanted array is never allocated at all?



I thought passing _ to out might do it, but it throws an error:



import numpy as np

ar = np.arange(6)/3

np.modf(ar, out=(ar, _))    



TypeError: return arrays must be of ArrayType


As it says in the docs, passing None means that the output array is allocated in the function and returned. I can ignore the returned values, but it still has to be allocated and populated inside the function.






python numpy




python numpy






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Nov 15 '18 at 23:12









Paul T.Paul T.

204210




204210








  • 1





    Why worry about this? There aren't many ufunc that return more than one array. How about using np.remainder(arr, 1) instead?

    – hpaulj
    Nov 15 '18 at 23:21













  • This was more of a question about the limits of ufunc than a particular use case. My additional reading is making me pretty sure it cannot be done.

    – Paul T.
    Nov 16 '18 at 0:29
















  • 1





    Why worry about this? There aren't many ufunc that return more than one array. How about using np.remainder(arr, 1) instead?

    – hpaulj
    Nov 15 '18 at 23:21













  • This was more of a question about the limits of ufunc than a particular use case. My additional reading is making me pretty sure it cannot be done.

    – Paul T.
    Nov 16 '18 at 0:29










1




1





Why worry about this? There aren't many ufunc that return more than one array. How about using np.remainder(arr, 1) instead?

– hpaulj
Nov 15 '18 at 23:21







Why worry about this? There aren't many ufunc that return more than one array. How about using np.remainder(arr, 1) instead?

– hpaulj
Nov 15 '18 at 23:21















This was more of a question about the limits of ufunc than a particular use case. My additional reading is making me pretty sure it cannot be done.

– Paul T.
Nov 16 '18 at 0:29







This was more of a question about the limits of ufunc than a particular use case. My additional reading is making me pretty sure it cannot be done.

– Paul T.
Nov 16 '18 at 0:29














1 Answer
1






active

oldest

votes


















4














You can minimize allocation by passing a "fake" array:



ar = np.arange(6) / 3

np.modf(ar, ar, np.broadcast_arrays(ar.dtype.type(0), ar)[0])


This dummy array is as big as a single double, and modf will not do allocation internally.



EDIT According to suggestions from @Eric and @hpaulj, a more general and long-term solution would be



np.lib.stride_tricks._broadcast_to(np.empty(1, ar.dtype), ar.shape, False, False)





share|improve this answer





















  • 1





    np.broadcast_to(np.empty((), dtype), shape) is a more general pattern, which works on non-numeric arrays too.

    – Eric
    Nov 16 '18 at 6:56













  • np.modf(ar, out=(ar, np.broadcast_to(np.empty((), ar.dtype), ar.shape))) does not work because the second output is read only

    – Paul T.
    Nov 16 '18 at 15:48











  • This is neat! The second output is still an array of the right length, but all elements point to the same location in memory.

    – Paul T.
    Nov 16 '18 at 15:57






  • 2





    Both broadcast_to and broadcast_arrays use np.lib.stride_tricks._broadcast_to. One uses a readonly=True parameter, the other sets it to False. But there's a TODO comment in the code about changing it to readonly, so in the long term this answer may need modification. It's interesting that the underlying tool in all of this is nditer.

    – hpaulj
    Nov 17 '18 at 5:35













Your Answer






StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");

StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});

function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});


}
});














draft saved

draft discarded


















StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53329181%2fnever-allocate-an-output-of-numpy-ufunc%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown

























1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









4














You can minimize allocation by passing a "fake" array:



ar = np.arange(6) / 3

np.modf(ar, ar, np.broadcast_arrays(ar.dtype.type(0), ar)[0])


This dummy array is as big as a single double, and modf will not do allocation internally.



EDIT According to suggestions from @Eric and @hpaulj, a more general and long-term solution would be



np.lib.stride_tricks._broadcast_to(np.empty(1, ar.dtype), ar.shape, False, False)





share|improve this answer





















  • 1





    np.broadcast_to(np.empty((), dtype), shape) is a more general pattern, which works on non-numeric arrays too.

    – Eric
    Nov 16 '18 at 6:56













  • np.modf(ar, out=(ar, np.broadcast_to(np.empty((), ar.dtype), ar.shape))) does not work because the second output is read only

    – Paul T.
    Nov 16 '18 at 15:48











  • This is neat! The second output is still an array of the right length, but all elements point to the same location in memory.

    – Paul T.
    Nov 16 '18 at 15:57






  • 2





    Both broadcast_to and broadcast_arrays use np.lib.stride_tricks._broadcast_to. One uses a readonly=True parameter, the other sets it to False. But there's a TODO comment in the code about changing it to readonly, so in the long term this answer may need modification. It's interesting that the underlying tool in all of this is nditer.

    – hpaulj
    Nov 17 '18 at 5:35


















4














You can minimize allocation by passing a "fake" array:



ar = np.arange(6) / 3

np.modf(ar, ar, np.broadcast_arrays(ar.dtype.type(0), ar)[0])


This dummy array is as big as a single double, and modf will not do allocation internally.



EDIT According to suggestions from @Eric and @hpaulj, a more general and long-term solution would be



np.lib.stride_tricks._broadcast_to(np.empty(1, ar.dtype), ar.shape, False, False)





share|improve this answer





















  • 1





    np.broadcast_to(np.empty((), dtype), shape) is a more general pattern, which works on non-numeric arrays too.

    – Eric
    Nov 16 '18 at 6:56













  • np.modf(ar, out=(ar, np.broadcast_to(np.empty((), ar.dtype), ar.shape))) does not work because the second output is read only

    – Paul T.
    Nov 16 '18 at 15:48











  • This is neat! The second output is still an array of the right length, but all elements point to the same location in memory.

    – Paul T.
    Nov 16 '18 at 15:57






  • 2





    Both broadcast_to and broadcast_arrays use np.lib.stride_tricks._broadcast_to. One uses a readonly=True parameter, the other sets it to False. But there's a TODO comment in the code about changing it to readonly, so in the long term this answer may need modification. It's interesting that the underlying tool in all of this is nditer.

    – hpaulj
    Nov 17 '18 at 5:35
















4












4








4







You can minimize allocation by passing a "fake" array:



ar = np.arange(6) / 3

np.modf(ar, ar, np.broadcast_arrays(ar.dtype.type(0), ar)[0])


This dummy array is as big as a single double, and modf will not do allocation internally.



EDIT According to suggestions from @Eric and @hpaulj, a more general and long-term solution would be



np.lib.stride_tricks._broadcast_to(np.empty(1, ar.dtype), ar.shape, False, False)





share|improve this answer















You can minimize allocation by passing a "fake" array:



ar = np.arange(6) / 3

np.modf(ar, ar, np.broadcast_arrays(ar.dtype.type(0), ar)[0])


This dummy array is as big as a single double, and modf will not do allocation internally.



EDIT According to suggestions from @Eric and @hpaulj, a more general and long-term solution would be



np.lib.stride_tricks._broadcast_to(np.empty(1, ar.dtype), ar.shape, False, False)






share|improve this answer














share|improve this answer



share|improve this answer








edited Nov 19 '18 at 2:32

























answered Nov 16 '18 at 3:28









ZisIsNotZisZisIsNotZis

745620




745620








  • 1





    np.broadcast_to(np.empty((), dtype), shape) is a more general pattern, which works on non-numeric arrays too.

    – Eric
    Nov 16 '18 at 6:56













  • np.modf(ar, out=(ar, np.broadcast_to(np.empty((), ar.dtype), ar.shape))) does not work because the second output is read only

    – Paul T.
    Nov 16 '18 at 15:48











  • This is neat! The second output is still an array of the right length, but all elements point to the same location in memory.

    – Paul T.
    Nov 16 '18 at 15:57






  • 2





    Both broadcast_to and broadcast_arrays use np.lib.stride_tricks._broadcast_to. One uses a readonly=True parameter, the other sets it to False. But there's a TODO comment in the code about changing it to readonly, so in the long term this answer may need modification. It's interesting that the underlying tool in all of this is nditer.

    – hpaulj
    Nov 17 '18 at 5:35
















  • 1





    np.broadcast_to(np.empty((), dtype), shape) is a more general pattern, which works on non-numeric arrays too.

    – Eric
    Nov 16 '18 at 6:56













  • np.modf(ar, out=(ar, np.broadcast_to(np.empty((), ar.dtype), ar.shape))) does not work because the second output is read only

    – Paul T.
    Nov 16 '18 at 15:48











  • This is neat! The second output is still an array of the right length, but all elements point to the same location in memory.

    – Paul T.
    Nov 16 '18 at 15:57






  • 2





    Both broadcast_to and broadcast_arrays use np.lib.stride_tricks._broadcast_to. One uses a readonly=True parameter, the other sets it to False. But there's a TODO comment in the code about changing it to readonly, so in the long term this answer may need modification. It's interesting that the underlying tool in all of this is nditer.

    – hpaulj
    Nov 17 '18 at 5:35










1




1





np.broadcast_to(np.empty((), dtype), shape) is a more general pattern, which works on non-numeric arrays too.

– Eric
Nov 16 '18 at 6:56







np.broadcast_to(np.empty((), dtype), shape) is a more general pattern, which works on non-numeric arrays too.

– Eric
Nov 16 '18 at 6:56















np.modf(ar, out=(ar, np.broadcast_to(np.empty((), ar.dtype), ar.shape))) does not work because the second output is read only

– Paul T.
Nov 16 '18 at 15:48





np.modf(ar, out=(ar, np.broadcast_to(np.empty((), ar.dtype), ar.shape))) does not work because the second output is read only

– Paul T.
Nov 16 '18 at 15:48













This is neat! The second output is still an array of the right length, but all elements point to the same location in memory.

– Paul T.
Nov 16 '18 at 15:57





This is neat! The second output is still an array of the right length, but all elements point to the same location in memory.

– Paul T.
Nov 16 '18 at 15:57




2




2





Both broadcast_to and broadcast_arrays use np.lib.stride_tricks._broadcast_to. One uses a readonly=True parameter, the other sets it to False. But there's a TODO comment in the code about changing it to readonly, so in the long term this answer may need modification. It's interesting that the underlying tool in all of this is nditer.

– hpaulj
Nov 17 '18 at 5:35







Both broadcast_to and broadcast_arrays use np.lib.stride_tricks._broadcast_to. One uses a readonly=True parameter, the other sets it to False. But there's a TODO comment in the code about changing it to readonly, so in the long term this answer may need modification. It's interesting that the underlying tool in all of this is nditer.

– hpaulj
Nov 17 '18 at 5:35






















draft saved

draft discarded




















































Thanks for contributing an answer to Stack Overflow!


  • Please be sure to answer the question. Provide details and share your research!

But avoid …



  • Asking for help, clarification, or responding to other answers.

  • Making statements based on opinion; back them up with references or personal experience.


To learn more, see our tips on writing great answers.




draft saved


draft discarded














StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53329181%2fnever-allocate-an-output-of-numpy-ufunc%23new-answer', 'question_page');
}
);

Post as a guest















Required, but never shown





















































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown

































Required, but never shown














Required, but never shown












Required, but never shown







Required, but never shown







-

Popular posts from this blog

Xamarin.iOS Cant Deploy on Iphone

Image
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ height:90px;width:728px;box-sizing:border-box; } -1 I have a windows laptop and a mac mini my problem is that It wont deploy on iphone if I use Visual Studio Xamarin on windows to install my app, but it works fine with Visual Studio For Mac. Here's what I get after build succeded on Visual Studio Xamarin on windows : 1>------ Build started: Project: FinalCustomerApp.iOS, Configuration: Debug iPhone ------ 1> Connecting to Mac server 192.168.8.100... 1> FinalCustomerApp.iOS -> C:UsersJeremy PaulDesktopFinalCustomerAppFinalCustomerAppFinalCustomerApp.iOSbiniPhoneDebugFinalCustomerApp.iOS.exe 1> Detected signing identity: 1> Code Signing Key: &quo
Read more

Glorious Revolution

Image
"The Bloodless Revolution" redirects here. For a history of the vegetarian movement, see The Bloodless Revolution (book). This article is about the English revolution of 1688. For the revolution of 1868 in Spain, see Glorious Revolution (Spain). For other uses, see Glorious Revolution (disambiguation). Glorious Revolution The Prince of Orange lands at Torbay Date 1688–1689 Location British Isles Also known as Revolution of 1688 War of the English Succession Bloodless Revolution Participants English, Welsh and Scottish society, Dutch forces Outcome Replacement of James II by William III and Mary II Jacobite rising of 1689 Williamite War in Ireland War with France; England and Scotland join Grand Alliance Drafting of the Bill of Rights 1689 Part of a series on the History of England Timeline Prehistoric Britain Roman Britain Sub-Roman Britain Medieval period Economy in the Middle Ages Anglo-Saxon period
Read more

Dulmage-Mendelsohn matrix decomposition in Python

Image
0 Matlab has a function called dmperm that computes the so-called Dulmage–Mendelsohn decomposition of a n x n matrix. From wikipedia, the Dulmage–Mendelsohn is a partition of the vertices of a bipartite graph into subsets, with the property that two adjacent vertices belong to the same subset if and only if they are paired with each other in a perfect matching of the graph. Looking both on scipy and numpy, I could not find this function, nor some similar version. Is it possible to implement it using basic linear algebra operations? Any idea if this is implemented in some Python package? python matlab linear-algebra graph-theory matrix-decomposition share | improve this question
Read more