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scientific-visualization

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opentk
josmuen
josmuen commented Mar 27, 2019

Description

We successfully moved our project from Tao to OpenTk a few years ago. Now we have an big issue in a multi terminal setup. 3D graphics is not properly drawn and the API concerning textures and lists behaves incorrect.
Our client workstation contains 2 NVIDA graphic cards (Quadro FX NVS 810/PCIe/SSE2) with 8 output ports each. Currently, 12 monitors are connected.

When we sta

razorx89
razorx89 commented Mar 15, 2019

Currently, the headless mode uses default parameters from PyChromeDevTools for connecting to a chrome headless instance.
https://github.com/maartenbreddels/ipyvolume/blob/e68b72852b61276f8e6793bc8811f5b2432a155f/ipyvolume/headless.py#L53
However, if you use a chrome headless running as docker instance and want to connect from another docker instance (e.g. when using docker-compose), the headl

pyvista
banesullivan
banesullivan commented Dec 29, 2019

I’m realizing that not all of the API is covered in the documentation. Is there a way we can run sphinx and report the coverage for what is autodoc’d in the docs?

It would be ideal to have this at 100%

banesullivan
banesullivan commented Jul 26, 2019

Feature request: allow passing custom made Matplotlib colormaps

import matplotlib.pyplot as plt
cmap = plt.cm.get_cmap("viridis", 5)

then pass that colormap to the Viewer

(the goal isn't necessarily catecorical colormaps... this is just a simple example. A user might want to make a custom normalized map that are far more complex)

psavery
psavery commented Nov 12, 2019

Currently, the raw data importer always assumes that the data is Fortran ordering, and the user has to manually transpose the data to Fortran ordering if it is actually C ordering instead.

We should add an option to the raw data importer so that the user can specify the ordering of the raw data file. If it is Fortran, we will do nothing, but if it is C ordering, we will convert it to Fortran or

inviwo
martinfalk
martinfalk commented Aug 30, 2019

When data is copied from a numpy array, it is always assumed that the data is contiguous. However, this is not necessarily the case for array views! In that case, one needs to create a deep copy before using this data with Inviwo, i.e. numpy.array(data, copy=True)

a = numpy.array([[1,2], [3,4], [5,6]])
b = a.flip(0)  # returns a view, cannot be used for a inviwo Layer / Volume
c 

an extensive Qt5 Plotter framework (including a feature-richt plotter widget, a speed-optimized, but limited variant and a LaTeX equation renderer!), written fully in C/C++ and without external dependencies

  • Updated Jun 29, 2020
  • C++
PawelTroka
PawelTroka commented May 3, 2017

For starters simple wiki with tutorials here on github should be more than enough. We should have here TSL examples in depth explanations and we should have ordinary help with screenshots explaining how to accomplish something.

In the aplication itself we could then just include help/tutorials button which would point here, to this new wiki.

tutorials and help https://github.com/PawelTroka/C

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