Commands#

nearl.commands.frame_voxelize(coords, weights, grid_dims, spacing, cutoff, sigma)[source]#

Voxelize a set of coordinates and weights (Single frame version of the density flow method)

Parameters:

coordsnp.ndarray: The coordinates of the points to be voxellized
weightsnp.ndarray: The weights of the points to be voxellized
grid_dimstuple: The dimensions of the grid
spacingfloat: The spacing of the grid
cutofffloat: The cutoff distance
sigmafloat: The sigma value for the Gaussian kernel

Returns:

np.ndarray: The voxellized grid sized grid_dims

Examples

>>> import numpy as np
>>> from nearl import commands
>>> coords = np.random.normal(size=(100, 3), loc=5, scale=2)
>>> weights = np.full(100, 1)
>>> grid_dims = np.array([32, 32, 32])
>>> commands.frame_voxelize(coords, weights, grid_dims, 0.5, 5, 2)

nearl.commands.frame_observation(coords, weights, grid_dims, spacing, cutoff, sigma, type_obs)[source]#

Perform marching observer on a single frame.

Parameters:

coordsnp.ndarray: The coordinates of the points to be voxellized
weightsnp.ndarray: The weights of the points to be voxellized
grid_dimstuple: The dimensions of the grid
spacingfloat: The spacing of the grid
cutofffloat: The cutoff distance
sigmafloat: The sigma value for the Gaussian kernel
type_obsint: The type of observer

Returns:

np.ndarray: The voxellized grid sized grid_dims

nearl.commands.density_flow(traj, weights, grid_dims, spacing, cutoff, sigma, type_agg)[source]#

Voxelize a trajectory using the density flow method

Parameters:

trajnp.ndarray: The trajectory to be voxellized
weightsnp.ndarray: The weights of the trajectory
grid_dimstuple: The dimensions of the grid
spacingfloat: The spacing of the grid
cutofffloat: The cutoff distance
sigmafloat: The sigma value for the Gaussian kernel
type_aggint: The type of aggregation function

Returns:

retgridnp.ndarray: The voxellized grid sized grid_dims

Examples

>>> import numpy as np
>>> from nearl import commands

nearl.commands.marching_observer(coords, weights, grid_dims, spacing, cutoff, type_obs, type_agg)[source]#

Marching observers algorithm to create a grid from a slice of frames. The number of atoms in each frame should be the same.

Parameters:

coordsnp.ndarray: The coordinates of the points to calculate the marching observer
weightsnp.ndarray: The weights of the corresponding points
grid_dimstuple: The dimensions of the grid
spacingfloat: The spacing of the grid
cutofffloat: The cutoff distance
type_obsint: The type of observer
type_aggint: The type of aggregation function

Returns:

ret_arrnp.ndarray: The voxellized grid sized grid_dims

Commands

Contents

Commands#