Find the next data element

Assignment operator definiton */

* Subdivide the list of points by a plane perpendicular on axe corresponding * to the 'cutfeat' dimension at 'cutval' position. * * On ret

Args: r: list[int, int]

Cat x with a positional encoding of x with scales 2^[min_deg, max_deg-1]. Instead of computing [sin(x), cos(x)], we use the trig identity cos(x)

Returns an indented representation of the nested dictionary.

Query for the num_closest closest points to a given point (entered as query_point[0:dim-1]).

Query for the \a num_closest closest points to a given point (entered as * query_point[0:dim-1]). Note that this is a short-cut method for * ind

* Find all the neighbors to \a query_point[0:dim-1] within a maximum radius. * The output is given as a vector of pairs, of which the first elemen

* Find all the neighbors to \a query_point[0:dim-1] within a maximum radius. * The output is given as a vector of pairs, of which the first elemen

Remove a point from the set (Lazy Deletion) */

Render all the pixels of an image (in test mode). Args: render_fn: function, jit-ed render function. rays: a `Rays` namedtuple, the rays to

jnp.cos() on a TPU may NaN out for large values.

jnp.sin() on a TPU may NaN out for large values.

Hierarchical sampling. Args: key: jnp.ndarray(float32), [2,], random number generator. bins: jnp.ndarray(float32), [batch_size, num_bins +

Stores the index in a binary file. * IMPORTANT NOTE: The set of data points is NOT stored in the file, so when * loading the index object it m

Stores the index in a binary file. * IMPORTANT NOTE: The set of data points is NOT stored in the file, so when * loading the index object it m

Save an image to disk. Args: img: jnp.ndarry, [height, width, channels], img will be clipped to [0, 1] before saved to pth. pth: stri

A cyclic and uniform colormap, see http://basecase.org/env/on-rainbows.

Returns number of points in dataset */

Transfer data to devices (GPU/TPU).

Args: - rvec: np.ndarray, [3, 1] - tvec: np.ndarray, [3, 1] Returns: - mat: np.ndarray, [4, 4]

One optimization step. Args: model: The linen model. rng: jnp.ndarray, random number generator. state: utils.TrainState, state of the m

Update the flags in `args` with the contents of the config YAML file.

* Computes the inde memory usage * Returns: memory used by the index */

Returns the length of each point in the dataset */

A wrapper around other visualizations for easy integration.

Volumetric Rendering Function. Args: rgb: jnp.ndarray(float32), color, [batch_size, num_samples, 3] density: jnp.ndarray(float32), sigma, [

* Find the worst result (furtherest neighbor) without copying or sorting * Pre-conditions: size() > 0 */

* Destructor. Frees all the memory allocated in this pool. */