What is the expected effect of where in numpy's negative?












2















As far as I understand the documentation of numpy's negative function, its where option allows you to leave some array components unnegated:



>>> import numpy as np

>>> np.negative(np.array([[1.,2.],[3.,4.],[5.,6.]]), where=[True, False])

array([[-1., 2.],

       [-3., 4.],

       [-5., 6.]])


However, when I try it, it seems that those values are (almost) zeroed instead:



>>> import numpy as np

>>> np.negative(np.array([[1.,2.],[3.,4.],[5.,6.]]), where=[True, False])

array([[-1.00000000e+000,  6.92885436e-310],

       [-3.00000000e+000,  6.92885377e-310],

       [-5.00000000e+000,  6.92885375e-310]])


So how should I see the where option?






python numpy







share|improve this question























  • Good question. Even a simpler example like np.negative(1., where=False) gives a similar wrong-looking, almost-zero result.

    – John Zwinck
    18 hours ago













  • np.negative(x, where=[True, False], out=x.copy()) will produce an array like x with selected values negated. where does not work meaningfully without an out.

    – hpaulj
    8 hours ago
















2















As far as I understand the documentation of numpy's negative function, its where option allows you to leave some array components unnegated:



>>> import numpy as np

>>> np.negative(np.array([[1.,2.],[3.,4.],[5.,6.]]), where=[True, False])

array([[-1., 2.],

       [-3., 4.],

       [-5., 6.]])


However, when I try it, it seems that those values are (almost) zeroed instead:



>>> import numpy as np

>>> np.negative(np.array([[1.,2.],[3.,4.],[5.,6.]]), where=[True, False])

array([[-1.00000000e+000,  6.92885436e-310],

       [-3.00000000e+000,  6.92885377e-310],

       [-5.00000000e+000,  6.92885375e-310]])


So how should I see the where option?






python numpy







share|improve this question























  • Good question. Even a simpler example like np.negative(1., where=False) gives a similar wrong-looking, almost-zero result.

    – John Zwinck
    18 hours ago













  • np.negative(x, where=[True, False], out=x.copy()) will produce an array like x with selected values negated. where does not work meaningfully without an out.

    – hpaulj
    8 hours ago














2












2








2








As far as I understand the documentation of numpy's negative function, its where option allows you to leave some array components unnegated:



>>> import numpy as np

>>> np.negative(np.array([[1.,2.],[3.,4.],[5.,6.]]), where=[True, False])

array([[-1., 2.],

       [-3., 4.],

       [-5., 6.]])


However, when I try it, it seems that those values are (almost) zeroed instead:



>>> import numpy as np

>>> np.negative(np.array([[1.,2.],[3.,4.],[5.,6.]]), where=[True, False])

array([[-1.00000000e+000,  6.92885436e-310],

       [-3.00000000e+000,  6.92885377e-310],

       [-5.00000000e+000,  6.92885375e-310]])


So how should I see the where option?






python numpy







share|improve this question














As far as I understand the documentation of numpy's negative function, its where option allows you to leave some array components unnegated:



>>> import numpy as np

>>> np.negative(np.array([[1.,2.],[3.,4.],[5.,6.]]), where=[True, False])

array([[-1., 2.],

       [-3., 4.],

       [-5., 6.]])


However, when I try it, it seems that those values are (almost) zeroed instead:



>>> import numpy as np

>>> np.negative(np.array([[1.,2.],[3.,4.],[5.,6.]]), where=[True, False])

array([[-1.00000000e+000,  6.92885436e-310],

       [-3.00000000e+000,  6.92885377e-310],

       [-5.00000000e+000,  6.92885375e-310]])


So how should I see the where option?






python numpy




python numpy






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked 18 hours ago









equaegheequaeghe

671724




671724













  • Good question. Even a simpler example like np.negative(1., where=False) gives a similar wrong-looking, almost-zero result.

    – John Zwinck
    18 hours ago













  • np.negative(x, where=[True, False], out=x.copy()) will produce an array like x with selected values negated. where does not work meaningfully without an out.

    – hpaulj
    8 hours ago



















  • Good question. Even a simpler example like np.negative(1., where=False) gives a similar wrong-looking, almost-zero result.

    – John Zwinck
    18 hours ago













  • np.negative(x, where=[True, False], out=x.copy()) will produce an array like x with selected values negated. where does not work meaningfully without an out.

    – hpaulj
    8 hours ago

















Good question. Even a simpler example like np.negative(1., where=False) gives a similar wrong-looking, almost-zero result.

– John Zwinck
18 hours ago







Good question. Even a simpler example like np.negative(1., where=False) gives a similar wrong-looking, almost-zero result.

– John Zwinck
18 hours ago















np.negative(x, where=[True, False], out=x.copy()) will produce an array like x with selected values negated. where does not work meaningfully without an out.

– hpaulj
8 hours ago





np.negative(x, where=[True, False], out=x.copy()) will produce an array like x with selected values negated. where does not work meaningfully without an out.

– hpaulj
8 hours ago












1 Answer
1






active

oldest

votes


















4














The documentation describes where like this:




Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.




Let's try an example using the out parameter:



x = np.ones(3)

np.negative(np.array([4.,5.,6.]), where=np.array([False,True,False]), out=x)


This sets x to [1., -5., 1.], and returns the same.



This makes some amount of sense once you realize that "leave the value in the output alone" literally means the output value is "don't care", rather than "same as the input" (the latter interpretation was how I read it the first time, too).



The problem comes in when you specify where but not out. Apparently the "ufunc machinery" (which is not visible in the implementation of np.negative()) creates an empty output array, meaning the values are indeterminate. So the locations at which where is False will have uninitialized values, which could be anything.



This seems pretty wrong to me, but there was a NumPy issue filed about it last year, and closed. It seems unlikely to change, so you'll have to work around it (e.g. by creating the output array yourself using zeros).






share|improve this answer























    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%2f54250461%2fwhat-is-the-expected-effect-of-where-in-numpys-negative%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














    The documentation describes where like this:




    Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.




    Let's try an example using the out parameter:



    x = np.ones(3)
    
np.negative(np.array([4.,5.,6.]), where=np.array([False,True,False]), out=x)


    This sets x to [1., -5., 1.], and returns the same.



    This makes some amount of sense once you realize that "leave the value in the output alone" literally means the output value is "don't care", rather than "same as the input" (the latter interpretation was how I read it the first time, too).



    The problem comes in when you specify where but not out. Apparently the "ufunc machinery" (which is not visible in the implementation of np.negative()) creates an empty output array, meaning the values are indeterminate. So the locations at which where is False will have uninitialized values, which could be anything.



    This seems pretty wrong to me, but there was a NumPy issue filed about it last year, and closed. It seems unlikely to change, so you'll have to work around it (e.g. by creating the output array yourself using zeros).






    share|improve this answer




























      4














      The documentation describes where like this:




      Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.




      Let's try an example using the out parameter:



      x = np.ones(3)
      
np.negative(np.array([4.,5.,6.]), where=np.array([False,True,False]), out=x)


      This sets x to [1., -5., 1.], and returns the same.



      This makes some amount of sense once you realize that "leave the value in the output alone" literally means the output value is "don't care", rather than "same as the input" (the latter interpretation was how I read it the first time, too).



      The problem comes in when you specify where but not out. Apparently the "ufunc machinery" (which is not visible in the implementation of np.negative()) creates an empty output array, meaning the values are indeterminate. So the locations at which where is False will have uninitialized values, which could be anything.



      This seems pretty wrong to me, but there was a NumPy issue filed about it last year, and closed. It seems unlikely to change, so you'll have to work around it (e.g. by creating the output array yourself using zeros).






      share|improve this answer


























        4












        4








        4







        The documentation describes where like this:




        Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.




        Let's try an example using the out parameter:



        x = np.ones(3)
        
np.negative(np.array([4.,5.,6.]), where=np.array([False,True,False]), out=x)


        This sets x to [1., -5., 1.], and returns the same.



        This makes some amount of sense once you realize that "leave the value in the output alone" literally means the output value is "don't care", rather than "same as the input" (the latter interpretation was how I read it the first time, too).



        The problem comes in when you specify where but not out. Apparently the "ufunc machinery" (which is not visible in the implementation of np.negative()) creates an empty output array, meaning the values are indeterminate. So the locations at which where is False will have uninitialized values, which could be anything.



        This seems pretty wrong to me, but there was a NumPy issue filed about it last year, and closed. It seems unlikely to change, so you'll have to work around it (e.g. by creating the output array yourself using zeros).






        share|improve this answer













        The documentation describes where like this:




        Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.




        Let's try an example using the out parameter:



        x = np.ones(3)
        
np.negative(np.array([4.,5.,6.]), where=np.array([False,True,False]), out=x)


        This sets x to [1., -5., 1.], and returns the same.



        This makes some amount of sense once you realize that "leave the value in the output alone" literally means the output value is "don't care", rather than "same as the input" (the latter interpretation was how I read it the first time, too).



        The problem comes in when you specify where but not out. Apparently the "ufunc machinery" (which is not visible in the implementation of np.negative()) creates an empty output array, meaning the values are indeterminate. So the locations at which where is False will have uninitialized values, which could be anything.



        This seems pretty wrong to me, but there was a NumPy issue filed about it last year, and closed. It seems unlikely to change, so you'll have to work around it (e.g. by creating the output array yourself using zeros).







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered 17 hours ago









        John ZwinckJohn Zwinck

        151k16176288




        151k16176288






























            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%2f54250461%2fwhat-is-the-expected-effect-of-where-in-numpys-negative%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

            Homophylophilia

            Image
            Haec pagina ad tempus protecta est ob maleficia perpetua. Quaesemus, emendationes suggere in pagina disputationis vel magistratum ad hanc deprotegendam pete. Homophylophilia Hyacinthus complexus ab amante suo Apolline, in pictura Mors Hyacinthi quam Jean Broc pinxit. Homophylophilia est sui sexus vel generis amor vel mores romantici vel sexuales, res venerea masculina in masculos, aut mulier in mulieres appetentes. Hoc ingenium sexuale est "exemplar permanens vel inclinatio ad experiendam sexualem, adfectionalem, vel romanticam vim attrahendi," praecipue vel solum ex hominibus eiusdem sexús; notio "etiam ad hominis iudicium identitatis privatae socialisque in illis attractionibus conditum refert, in moribus quoque qui illos exprimant, et in societate communitatis aliorum qui illos communicent." [1] [2] Per aevos alii aliter rem definiverunt, sed Occidentali nostrae aetatis cultu civili non tantum ad ipsa acta erotica spectat, sed etiam igitur
            Read more

            Updating UILabel text programmatically using a function

            Image
            0 so I am trying to update the UIlabel text using a func. I have seen few other questions like mine and I tried their method but it doesn't work. fun returns no value when used in the UIlabel. I have shown my code below.It has to be noted that when I use the function in viewdidload I can retrieve the data. lazy var name: UILabel = { let lb = UILabel() lb.text? = getthename() lb.lineBreakMode = NSLineBreakMode.byWordWrapping lb.textColor = .mainGreen lb.textAlignment = .left lb.font = UIFont.boldSystemFont(ofSize:12.0) return lb }() func getthename() -> String { var wins : String = "" let ud = (Auth.auth().currentUser?.uid) self.ref = Database.database().reference() self.ref.child("Users").child(ud!).observeSingleEvent(of: .value, with: {(snapshot) in
            Read more

            Cloud Functions - OpenCV Videocapture Read method fails for larger files from cloud storage

            Image
            0 I created a cloud function to read a video file from cloud storage and read frames and create an image for each frame. This function works for small files which is 1-2 MB, However, this method fails while reading larger file 130 MB. import cv2 from google.cloud import storage vidcap = cv2.VideoCapture("https://storage.googleapis.com/[bucker_name]/[filename.mp4]") if vidcap.isOpened(): vidcap.open("https://storage.googleapis.com/[bucker_name]/[filename.mp4]") success,image = vidcap.read() print(success) Expected the print will return true and an image will hold an image of a first frame. python-3.x function opencv cloud share | improve this question
            Read more