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@@ -86,7 +88,7 @@ it's not included with the official release, but is found in
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This should be hidden...
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--->
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### v1.4.0 has NOT been released (2024/2/29)
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### v1.4.0 has been released (2024/2/29)
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It's been a while since the the update, but I've had a more time lately. MSC also provided a copy of MSC Nastran free of charge to help support the project, so modern MSC Nastran support is much better.
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@@ -123,7 +125,8 @@ GUI:
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- greatly expanded preferences menu; many more preferences are saved
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- new icons
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See pyNastran `v1.4.0 release notes <https://github.com/SteveDoyle2/pyNastran/releases/tag/v1.4.0>`_ for details on the other changes.
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See pyNastran `release notes <https://github.com/SteveDoyle2/pyNastran/releases/tag/v1.4.0>`_ for details on the other changes.
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### v1.3.4 has been released (2022/5/30)
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There are also at least 33 bugs fixed and a few features added from the 1.4 release (e.g., pathlib support, subcase limiting in the flutter F06 parser).
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See pyNastran `v1.3.4 release notes <https://github.com/SteveDoyle2/pyNastran/releases/tag/v1.3.4>`_ for details on the other changes.
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See pyNastran [release notes](https://github.com/SteveDoyle2/pyNastran/releases/tag/1.3.4) for details on the other changes.
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### v1.3.3 (2020/6/28)
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- updated nptyping requirements
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- support for NX 2019.0, 2019.1
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See pyNastran `v1.3.3 release notes <https://github.com/SteveDoyle2/pyNastran/releases/tag/v1.3.3>`_ for details on the 54 bug fixes. Not too bad for 2.5 months!
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See pyNastran [release notes](https://github.com/SteveDoyle2/pyNastran/releases/tag/1.3.3) for details on the 54 bug fixes. Not too bad for 2.5 months!
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### pyNastran v1.3.2 has been released (2020/4/8)
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- options for Nastran in preferences menu to speed up loading/limit memory usage
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- pyNastran BDF pickle reading
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- pyNastran OP2HDF5 reading (notMSC's format)
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- visualization when pickling nodes/elements
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- visualization when picking nodes/elements
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-min/max labels
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- highlight menu
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- Patran-style colors
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- custom force vectors
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-AVL support
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Known issues:
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- Transient Pandas Dataframes will fail for newer versions of numpy/pandas.
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If anyone knows how to use a MultiIndex, this is probably pretty easy to fix.
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