Q: Installing Taichi with pip, complains package not found.
A: Is your Python version >= 3.6, and 64-bit? See :ref:`troubleshooting`.
Q: Do we have something like ti.pi?
A: No, but you may use math.pi or numpy.pi instead. Taichi is able to bake in these constants during JIT, so your kernels incur no runtime cost.
Q: How do I force an outermost loop to be serial, i.e. not parallelized?
A: Try this trick:
for _ in range(1): # I'm the outer-most loop!
for i in range(100): # This loop will not be parallelized
...Q: What's the most convinent way to load images / textures into Taichi tensors?
A: Simply use tensor.from_numpy(ti.imread('filename.png')).
Q: Can Taichi co-operate with other Python packages like matplotlib?
A: Yes, as long as that package provides an interface with numpy, see :ref:`other_python_packages`.
Q: Shall we add some handy functions like ti.smoothstep or ti.vec3?
A: No, but we provide them in an extension library Taichi GLSL <https://taichi-glsl.readthedocs.io>, install it using:
python -m pip install taichi_glslQ: How can I render 3D results without writing a ray tracer myself?
- A: You may export it with :ref:`export_ply_files` so that you could view it in Houdini or Blender.
- Or make use the extension library Taichi THREE <https://github.com/taichi-dev/taichi_glsl> to render images and update to GUI in real-time.
Q: How do I declare a tensor with dynamic length?
- A: What you want may be the
dynamicSNode, a kind of sparse tensor, see :ref:`dynamic`. - Or simply allocate a dense tensor large enough, and another 0-D tensor
tensor_len[None]for length record. But in fact, thedynamicSNode could be slower than the latter solution, due to the cost of maintaining the sparsity information.
Q: Can a user iterate over irregular topologies (e.g., graphs or tetrahedral meshes) instead of regular grids?
- A: These structures have to be represented using 1D arrays in Taichi. You can still iterate over them using
for i in xorfor i in range(n). - However, at compile time, there's little the Taichi compiler can do for you to optimize it. You can still tweak the data layout to get different runtime cache behaviors and performance numbers.