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

Bressuire

Image
Subprefecture and commune in Nouvelle-Aquitaine, France Bressuire Subprefecture and commune Chateau de Bressuire and the Eglise Notre-Dame Coat of arms Location of Bressuire Bressuire Show map of France Bressuire Show map of Nouvelle-Aquitaine Coordinates: 46°50′27″N 0°29′14″W  /  46.8408°N 0.4872°W  / 46.8408; -0.4872 Coordinates: 46°50′27″N 0°29′14″W  /  46.8408°N 0.4872°W  / 46.8408; -0.4872 Country France Region Nouvelle-Aquitaine Department Deux-Sèvres Arrondissement Bressuire Canton Bressuire Government  • Mayor .mw-parser-output .nobold{font-weight:normal} (2014–20) Jean Michel Bernier Area 1 180.59 km 2 (69.73 sq mi) Population (2014) 2 19,300  • Density 110/km 2 (280/sq mi) Time zone UTC+01:00 (CET)  • Summer (DST) UTC+02:00 (CEST) INSEE/Postal code 79049 /79300 Elevation 98–236 m (322–774 ft) (avg. 173 m or 568 ft) 1 French Land Register data, which exclude...
Read more

Vorschmack

Image
Vorschmack Ukrainian Jewish-style vorschmack served on rye bread Course Hors d'oeuvre Region or state Eastern Europe Associated national cuisine Ashkenazi Jewish, Finnish, German, Ukrainian, Polish, Russian Main ingredients Ground meat and/or fish Cookbook: Vorschmack   Media: Vorschmack Vorschmack or forshmak (Yiddish: פֿאָרשמאַק ‎, from archaic German Vorschmack , "foretaste" [1] or "appetizer" [2] ) is an originally East European dish made of salty minced fish or meat. Different variants of this dish are especially common in Ashkenazi Jewish and Finnish cuisine. Some varieties are also known in Russian and Polish cuisine. Contents 1 In Jewish cuisine 2 In Russian cuisine 3 In Polish cuisine 4 In Finnish cuisine 5 See also 6 References In Jewish cuisine According to Gil Marks, the German name points to the possible Germanic origin of this dish. [1] William Pokhlyobkin descr...
Read more

Quarantine

Image
For other uses, see Quarantine (disambiguation). Signal flag "Lima" called the "Yellow Jack" which when flown in harbor means ship is under quarantine. A simple yellow flag (also called the "Yellow Jack") had historically been used to signal quarantine (it stands for Q among signal flags), but now indicates the opposite, as a signal of a ship free of disease that requests boarding and inspection. A quarantine is used to separate and restrict the movement of people; it is 'a restraint upon the activities or communication of persons or the transport of goods designed to prevent the spread of disease or pests', for a certain period of time. [1] This is often used in connection to disease and illness, such as those who may possibly have been exposed to a communicable disease. [2] The term is often erroneously used to mean medical isolation, which is "to separate ill persons who have a communicable disease from those who are healthy...
Read more