Multiscale Time-dependent Visual Abstraction Framework for Exploratory Analysis of DNA Nanotechnology Simulations
Case study video: https://youtu.be/Hu0tfpS7JBk
Feedback form: https://forms.gle/MH2F7mkDQVHpfNUv6
The software has been tested on Windows and Max; however, because a high-end MacBook Pro cannot maintain an interactive frame rate, we do not recommend using SynopFrame on MacBooks.
Windows installation guide.
For Linux users, the installation process should be similar to Windows. We are happy to guide you through the process.
- In case you cannot launch Houdini after installation, and the error message is similar to
Crash report from <YourName>; Houdini FX Version 19.5.493 [linux-x86_64-gcc9.3]
Uptime 2 seconds
Tue Feb 14 11:41:13 2023
Caught signal 11
Traceback from 14123 ThreadId=0x7fc00cfb5e80
AP_Interface::coreDumpChaser(UTsignalHandlerArg) <libHoudiniUI.so>
AP_Interface::si_CrashHandler::chaser(UTsignalHandlerArg) <libHoudiniUI.so>
signalCallback(UTsignalHandlerArg) <libHoudiniUT.so>
UT_Signal::UT_ComboSignalHandler::operator()(int, siginfo_t*, void*) const <libHoudiniUT.so>
UT_Signal::processSignal(int, siginfo_t*, void*) <libHoudiniUT.so>
__funlockfile <libpthread.so.0>
As suggested here, you may run sudo apt-get install lsb-core and try again.
- You will need to find the Houdini preference folder (on Windows, it is
C:\Users\<Your_User_Name>\Documents\houdini19.5). It is likely under your home foler.
To run SynopFrame for your own dataset, you need to create a folder with the following contents.
└─<case_folder_name>
├─input
│ ├─design.oxdna.dat
│ ├─design.oxdna.top
│ ├─design.synopspace.hb
│ ├─mean.oxdna.dat
│ ├─trajectory_run.synopspace.hb
│ ├─trajectory_run.synopspace.pca_coords
│ └─trajectory_run_aligned_to_mean.oxdna.dat
└─<structure_name>.hipnc # this is copied from e.g. `demo_cube.hipnc`Check the Input-Specification for the meaning of each file.
After all the files are prepared, open <structure_name>.hipnc, go to the IO_and_Controller panel, the IO tab, change the print_conf_interval accordingly. And then click Cache All. After the data is cached, you dataset is then at the same state as the demo dataset and is ready to analyze.
Please use the GitHub Issues.