Hello! I've recently discovered yt, and I found the rendering examples really encouraging. I'm now trying to visualize a large 3D dataset via volume rendering technique. I have come up with the following script that is intended to plot a part of the dataset: --- import yt from yt.config import ytcfg import h5py f = h5py.File('ims-p.hdf5') intens = f['intensity'][:256,:256,:256] print(intens.shape) ds = yt.load_uniform_grid({'Intensity': intens}, intens.shape) print(ds) sc = yt.create_scene(ds, ('Intensity'), lens_type = 'perspective') sc[0].set_log(True) sc[0].tfh.plot('transfer_function.png', profile_field = 'Intensity') sc.camera.resolution = 1024 sc.annotate_axes(alpha = 0.1) sc.annotate_domain(ds, color = [1, 1, 1, 0.1]) sc.save_annotated('vr_grids.png', sigma_clip = 4) --- I get some really fascinating rendering (attached), and now I want to tweak it. I would be really happy if you could help me with the following questions: 1) What is the best way to configure the camera viewpoint so the complete data cube fits into the view? How to quickly render the domain axes without performing the costly volume rendering (to find the "optimal" viewpoint experimentally)? 2) What is the best way to specify axes scales, so that rendered volume appears as cuboid, not cube? I want the resulting cuboid to be scaled as 3:2:2. How to specify resulting image size, e.g. how to get image of 1600x1200 pixels? 3) In my dataset, the grid is not in fact uniform, but rather X coordinates are specified by (non-decreasing) array rettime, Y coordinates are specified by (non-decreasing) array mz. What is the best way to pass this information (to load_uniform_grid)? 4) Is it possible to use inverse transfer function model, i.e. white background? 5) My complete dataset is rather large and doesn't readily fit into RAM. However it seems that for ray-tracing algorithm, it shouldn't be required that all data is available simultaneously. Is it possible to feed dataset by chunks? Your help is greatly appreciated! Best wishes, Andrey Paramonov -- This message has been scanned for viruses and dangerous content by MailScanner, and is believed to be clean.