![]() The passing of data between VTK and numpy should by very efficient. ![]() Second, you say that you are trying to minimize the vtk numpy transformations. I don’t think I understand your problem well enough to give any advice one way or another. Whether or not you split filters into multiple is a style decision. I think what you actually mean is a filter, which is what we call those units that are displayed in the Pipeline Browser. It makes little sense to me to break up a plugin into pieces since you can wrap up lots of different filters into a single plugin. They are typically incased in a shared object library or sometimes as an xml (see the plugin documentation for more). I will try to give some feedback on some of the points you make.įirst, I am a bit confused by what you mean by a “plugin.” In ParaView, plugins are a specific thing that can encapsulate a number of objects such as filters, rendering components, GUI elements, and code to load at startup. Your description and question you ask is very broad, and I don’t think I can give any definitive answer for everything that you ask. Thank you for your time, and thanks in advance for any hint, Was there any successful attempt to compile a python extension into Paraview with cython? So best is that, during the compilation of the python extension, we use the same environment as the one provided by Paraview. The version of numpy might however not match the one that comes from the system or any virtualenv. Paraview on OSX and Linux are both compiled against the system python, which is a good news for ABI compatibility. Is there a flag or a hash that might be used to indicate that the incoming data has changed since last processing?įinally, to accelerate the processing, some python parts might be compiled as python extensions. is there a way to have an information about the upstream pipeline? If the pipeline changes somehow, some computation need to be executed again with the new incoming data.is there a way to store information in a global memory, such that it can be reused the next time the pipeline executes the plugin? Is there a best disk location to cache information on disk?.The other option would be caching some information that does not need to be recomputed. Is a programmable source working the same? I am currently using examples for generating the XML out of some python description from there: and that I adapted to my needs. Yet, I would like to be able to change some of the parameters of the source. if the processing plugin does not need any input (just a file that is outside of the pipeline), then it would make sense to make it a programmable source.I am asking this because the targeted users are not techy at all. is the user required to manually add those 2 plugins in the pipeline? Is there a way to hide this, in the sense that the end user does not need to know there are 2 plugins? For instance, the user selects my plugin, and automatically two elements are added to the pipeline without the user doing any manual configuration.if so, how do I name the array that is being passed downstream? how do I distinguish between the different sources of information?.is it possible a plugin to output a numpy array, and this array being passed to downstream plugins?.Any hint would be more than welcome:ĭivide the plugin into 2 or 3: a processing plugin and a graphic primitive plugin. Here are some ideas, but I do not know how to achieve those. The processing is performed in numpy, so there are vtknumpy transformations I would like to keep as minimal as possible. Especially, I want to avoid reprocessing everything is the user change some visualization parameter from 3/ (for instance, he/she selects a specific trajectory or primitive that should be shown or hidden). The step 2 is the most time consuming, and ideally I want to avoid any recomputation that is unnecessary. some parameters that affect the rendering of the plugin.some parameters that affect the processing performed by the plugin.an external file (numpy array) that is transformed depending on the mesh.Python obviously is not the fastest programming language, but this apart, I would like to have some better insights on how to shape the overall architecture of the plugin such that it scales better to computations and interactions. My question concern general design of a python programmable filter, and how to improve the user “experience” by making things faster and easier to interact with. I am relatively new to Paraview/VTK and plugin developments, so please accept my apologies if my questions seem naive. My name is Raffi Enficiaud, and I am developing a plugin in Paraview for brain data visualization.
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