Dataset for the challenge at the 2nd MODE workshop on differentiable programming 2022

Data is in HDF5 format (with LZF compression). For specifics and details, please see https://github.com/GilesStrong/mode_diffprog_22_challenge The training file contains two datasets: `'x0'`: a set of voxelwise X0 predictions (float32) `'targs'`: a set of voxelwise classes (int): 0 = soil 1 = wall The format of the datasets is a rank-4 array, with dimensions corresponding to (samples, z position, x position, y position). All passive volumes are of the same size: 10x10x10 m, with cubic voxels of size 1x1x1 m, i.e. every passive volume contains 1000 voxels. The arrays are ordered such that zeroth z layer is the bottom layer of the passive volume, and the ninth layer is the top layer. It can be read using e.g. the code below: <em>with h5py.File('train.h5', 'r') as f:</em> <em> inputs = h5['x0'][()]</em> <em> targets = h5['targs'][()]</em> The test file only contains the X0 inputs: <em>with h5py.File('test.h5', 'r') as h5:</em> <em> inputs = h5['x0'][()]</em> The private testing sample also contains targets. The private and public splits can be recovered using: <em>from sklearn.model_selection import train_test_split</em> <em>pub, pri = train_test_split(targets, test_size=25000, random_state=3452, shuffle=True)</em>