generate_side_by_side.py [349:392]:
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            if i == 0:
                # Only calculate the histograms once; it takes time
                datan, new_orange_frameind = _get_frames(new_videos[j])
                datan = np.asarray(datan)
                new_orange_frameinds.append(new_orange_frameind)

                histn, _, _ = plt.hist(datan.flatten(), bins=255)
                plt.clf()
                gc.collect()

                nhists.append(histn)
            else:
                histn = nhists[j]

            rho, _ = spearmanr(histo, histn)

            xcorr[i, j] = rho

    finish_same_line()

    similarity = np.nanmean(xcorr)

    print("Average 3D similarity: %s" % str(np.round(similarity, 5)))

    if most_similar:
        inds = np.unravel_index(np.argmax(xcorr, axis=None), xcorr.shape)
    else:
        inds = np.unravel_index(np.argmin(xcorr, axis=None), xcorr.shape)

    oldvid = old_videos_info[inds[0]]["path"]
    oldvidnewpath = str(pathlib.Path(output, "%sold_video.mp4" % prefix))
    shutil.copyfile(oldvid, oldvidnewpath)

    newvid = new_videos_info[inds[1]]["path"]
    newvidnewpath = str(pathlib.Path(output, "%snew_video.mp4" % prefix))
    shutil.copyfile(newvid, newvidnewpath)

    return {
        "sim3": np.round(similarity, 5),
        "oldvid": oldvidnewpath,
        "oldvid_ind": old_orange_frameinds[inds[0]],
        "newvid": newvidnewpath,
        "newvid_ind": new_orange_frameinds[inds[1]],
    }
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mozperftest_tools/mozperftest_tools/side_by_side.py [492:535]:
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                if i == 0:
                    # Only calculate the histograms once; it takes time
                    datan, new_orange_frameind = _get_frames(new_videos[j])
                    datan = np.asarray(datan)
                    new_orange_frameinds.append(new_orange_frameind)

                    histn, _, _ = plt.hist(datan.flatten(), bins=255)
                    plt.clf()
                    gc.collect()

                    nhists.append(histn)
                else:
                    histn = nhists[j]

                rho, _ = spearmanr(histo, histn)

                xcorr[i, j] = rho

        finish_same_line()

        similarity = np.nanmean(xcorr)

        print("Average 3D similarity: %s" % str(np.round(similarity, 5)))

        if most_similar:
            inds = np.unravel_index(np.argmax(xcorr, axis=None), xcorr.shape)
        else:
            inds = np.unravel_index(np.argmin(xcorr, axis=None), xcorr.shape)

        oldvid = old_videos_info[inds[0]]["path"]
        oldvidnewpath = str(pathlib.Path(output, "%sold_video.mp4" % prefix))
        shutil.copyfile(oldvid, oldvidnewpath)

        newvid = new_videos_info[inds[1]]["path"]
        newvidnewpath = str(pathlib.Path(output, "%snew_video.mp4" % prefix))
        shutil.copyfile(newvid, newvidnewpath)

        return {
            "sim3": np.round(similarity, 5),
            "oldvid": oldvidnewpath,
            "oldvid_ind": old_orange_frameinds[inds[0]],
            "newvid": newvidnewpath,
            "newvid_ind": new_orange_frameinds[inds[1]],
        }
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