|
634 | 634 | " return (raw - dark) / (flat - dark)" |
635 | 635 | ] |
636 | 636 | }, |
| 637 | + { |
| 638 | + "cell_type": "markdown", |
| 639 | + "metadata": { |
| 640 | + "slideshow": { |
| 641 | + "slide_type": "fragment" |
| 642 | + } |
| 643 | + }, |
| 644 | + "source": [ |
| 645 | + "*note: if you like to plot an image you can use the imshow command !!! the %pylab should be called once before calling the imshow function !!!*" |
| 646 | + ] |
| 647 | + }, |
| 648 | + { |
| 649 | + "cell_type": "code", |
| 650 | + "execution_count": null, |
| 651 | + "metadata": { |
| 652 | + "slideshow": { |
| 653 | + "slide_type": "fragment" |
| 654 | + } |
| 655 | + }, |
| 656 | + "outputs": [], |
| 657 | + "source": [ |
| 658 | + "%matplotlib inline\n", |
| 659 | + "\n", |
| 660 | + "from matplotlib import pyplot as plt" |
| 661 | + ] |
| 662 | + }, |
| 663 | + { |
| 664 | + "cell_type": "code", |
| 665 | + "execution_count": null, |
| 666 | + "metadata": { |
| 667 | + "slideshow": { |
| 668 | + "slide_type": "fragment" |
| 669 | + } |
| 670 | + }, |
| 671 | + "outputs": [], |
| 672 | + "source": [ |
| 673 | + "import numpy\n", |
| 674 | + "plt.imshow(numpy.random.random((20, 60)))" |
| 675 | + ] |
| 676 | + }, |
637 | 677 | { |
638 | 678 | "cell_type": "markdown", |
639 | 679 | "metadata": { |
|
702 | 742 | "# ...\n" |
703 | 743 | ] |
704 | 744 | }, |
705 | | - { |
706 | | - "cell_type": "markdown", |
707 | | - "metadata": { |
708 | | - "slideshow": { |
709 | | - "slide_type": "fragment" |
710 | | - } |
711 | | - }, |
712 | | - "source": [ |
713 | | - "*note: if you like to plot an image you can use the imshow command !!! the %pylab should be called once before calling the imshow function !!!*" |
714 | | - ] |
715 | | - }, |
716 | | - { |
717 | | - "cell_type": "code", |
718 | | - "execution_count": null, |
719 | | - "metadata": { |
720 | | - "slideshow": { |
721 | | - "slide_type": "fragment" |
722 | | - } |
723 | | - }, |
724 | | - "outputs": [], |
725 | | - "source": [ |
726 | | - "%matplotlib inline\n", |
727 | | - "\n", |
728 | | - "from matplotlib import pyplot as plt" |
729 | | - ] |
730 | | - }, |
731 | | - { |
732 | | - "cell_type": "code", |
733 | | - "execution_count": null, |
734 | | - "metadata": { |
735 | | - "slideshow": { |
736 | | - "slide_type": "fragment" |
737 | | - } |
738 | | - }, |
739 | | - "outputs": [], |
740 | | - "source": [ |
741 | | - "import numpy\n", |
742 | | - "plt.imshow(numpy.random.random((20, 60)))" |
743 | | - ] |
744 | | - }, |
745 | 745 | { |
746 | 746 | "cell_type": "markdown", |
747 | 747 | "metadata": { |
|
816 | 816 | "\n", |
817 | 817 | "This can be an issue, e.g., during acquisition.\n", |
818 | 818 | "\n", |
819 | | - "WARNING: With file locking disabled, do not open twice the same file for writing or the file will be corrupted.To disable file locking, open the HDF5 file this way:" |
| 819 | + "WARNING: With file locking disabled, do not open twice the same file for writing or the file will be corrupted. To disable file locking, open the HDF5 file this way:" |
820 | 820 | ] |
821 | 821 | }, |
822 | 822 | { |
|
875 | 875 | "cell_type": "markdown", |
876 | 876 | "metadata": { |
877 | 877 | "slideshow": { |
878 | | - "slide_type": "subslide" |
| 878 | + "slide_type": "fragment" |
879 | 879 | } |
880 | 880 | }, |
881 | 881 | "source": [ |
|
973 | 973 | "* an example on [how to store tomography raw data](http://download.nexusformat.org/doc/html/classes/applications/NXtomo.html?highlight=tomography)\n", |
974 | 974 | "* an example to store [tomoraphy application (3D reconstruction)](http://download.nexusformat.org/doc/html/classes/applications/NXtomoproc.html?highlight=tomography)\n" |
975 | 975 | ] |
976 | | - }, |
977 | | - { |
978 | | - "cell_type": "code", |
979 | | - "execution_count": null, |
980 | | - "metadata": {}, |
981 | | - "outputs": [], |
982 | | - "source": [] |
983 | 976 | } |
984 | 977 | ], |
985 | 978 | "metadata": { |
|
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