xyz_data

Basic utilities for working with the xyz file format

class auvlib.data_tools.xyz_data.cloud

Class for xyz point cloud type

static from_matrix(arg0: numpy.ndarray[float64[m, n]]) → List[numpy.ndarray[float64[3, 1]]]

Create Points from an Nx3 matrix

static from_pings(arg0: List[auvlib.data_tools.std_data.mbes_ping]) → List[List[numpy.ndarray[float64[3, 1]]]]

Create vector of xyz_data::Points from std_data::mbes_ping::PingsT by splitting at first_in_file_==True

static parse_file(arg0: unicode) → List[numpy.ndarray[float64[3, 1]]]

Parse xyz_data::Points from .xyz file

static parse_folder(arg0: unicode) → List[numpy.ndarray[float64[3, 1]]]

Parse xyz_data::Points from folder of .xyz files

static read_data(arg0: unicode) → List[numpy.ndarray[float64[3, 1]]]

Read xyz_data::Points from .cereal file

static to_matrix(arg0: List[numpy.ndarray[float64[3, 1]]]) → numpy.ndarray[float64[m, n]]

Create an Nx3 matrix from the list of points

auvlib.data_tools.xyz_data.subsample_points(arg0: List[numpy.ndarray[float64[3, 1]]], arg1: int) → List[numpy.ndarray[float64[3, 1]]]

Subsample by skipping N points at a time

auvlib.data_tools.xyz_data.transform_points(arg0: numpy.ndarray[float64[4, 4]], arg1: List[numpy.ndarray[float64[3, 1]]]) → List[numpy.ndarray[float64[3, 1]]]

Transform using 4x4 transformation matrix T

auvlib.data_tools.xyz_data.write_data(arg0: List[numpy.ndarray[float64[3, 1]]], arg1: unicode) → None

Write array of Vector3d to .cereal file