astrohack.utils.ray_tracing_general

Attributes

nanvec3d
return_line

Classes

GlobalQPS
LocalQPS

Functions

generalized_dot(vec_map_a, vec_map_b)
generalized_norm(vecmap)
generalized_dist(vec_map_a, vec_map_b)
normalize_vector_map(vector_map)
reflect_light(light, normals)
simple_axis(minmax, resolution[, margin])	Creates an array spaning from min to max (may go over max if resolution is not an integer division) spaced by resolution
np_qps_fitting(pcd)
degrade_pcd(pcd, degrading_factor)	Degrades de number of points in a pcd by an integer factor
qps_compute_point_and_normal(pnt, qps_coeffs, pcd)
qps_compute_point_and_normal_jit(pnt, qps_coeffs, pcd)
qps_compute_normal_jit(pnt, qps_coeffs, pcd)
local_qps_image_jit(global_pcd, local_qps_coeffs, ...)
global_qps_image_jit(pcd, qps_coeffs, points)
global_qps_normal_image_jit(pcd, qps_coeffs, points)

Module Contents

nanvec3d

return_line = '\x1b[F'

generalized_dot(vec_map_a, vec_map_b)

generalized_norm(vecmap)

generalized_dist(vec_map_a, vec_map_b)

normalize_vector_map(vector_map)

reflect_light(light, normals)

simple_axis(minmax, resolution, margin=0.05)

Creates an array spaning from min to max (may go over max if resolution is not an integer division) spaced by resolution :param minmax: the minimum and maximum of the axis :param resolution: The spacing between array elements :param margin: Add a margin at the edge of the array beyonf min and max

Returns:

A numpy array representation of a linear axis.

np_qps_fitting(pcd)

degrade_pcd(pcd, degrading_factor)

Degrades de number of points in a pcd by an integer factor :param pcd: pcd data, assumes [:, 3] :param degrading_factor: integer pcd degrading factor

Returns: degraded pcd

qps_compute_point_and_normal(pnt, qps_coeffs, pcd)

qps_compute_point_and_normal_jit(pnt, qps_coeffs, pcd)

qps_compute_normal_jit(pnt, qps_coeffs, pcd)

local_qps_image_jit(global_pcd, local_qps_coeffs, local_pcds, points)

global_qps_image_jit(pcd, qps_coeffs, points)

global_qps_normal_image_jit(pcd, qps_coeffs, points)

class GlobalQPS

n_qps_extra_vars = 6

n_points = None

point_cloud = None

qps_coefficients = None

classmethod from_point_cloud(pcd_data, degradation_factor=None, displacement=(0, 0, 0))

classmethod from_point_cloud_and_coefficients(point_cloud, qps_coefficients)

classmethod from_pickle(filename)

to_pickle(filename)

classmethod from_zarr(filepath)

to_zarr(filepath)

compute_gridded_z_cos(u_axis, v_axis, mask, light=(0, 0, 1))

compute_gridded_z_val_and_z_cos(u_axis, v_axis, mask, light=(0, 0, 1))

class LocalQPS

n_qps_extra_vars = 6

npnt = -1

local_qps_n_pnt = -1

global_pcd = None

local_qps_coeffs = None

local_pcds = None

current_z_val = None

current_z_cos = None

current_u_axis = None

current_v_axis = None

high_res_z_val = None

high_res_z_cos = None

high_res_u_axis = None

high_res_v_axis = None

classmethod from_pcd(pcd_data, local_qps_n_pnt=20, displacement=(0, 0, 0))

export_to_xr_data_variables()

Idea is to return pcd values and qpd coeffs as Xarray data variables for storage. Returns: xarray data variables

classmethod from_xr_data_variable()

Idea is to init an object from a few xarray Data variables from storage Returns: initialized obj

compute_z_val_and_z_cos(point)

Idea is to compute value of QPS and angle with boresight :param point:

Returns:

vectorized_z_val_and_z_cos(point_arr)

plot_z_val_and_z_cos(colormap='viridis', zlim=None, dpi=300, display=False)

compute_gridded_z_val_and_z_cos(u_axis, v_axis, mask, gridding_engine='2D regrid', light=(0, 0, -1), vectorized=True)

downgrid_high_resolution_z_val_and_z_cos(u_axis, v_axis, gridding_engine)

compute_high_resolution_z_val_and_z_cos(x_resolution, y_resolution)

classmethod from_pickle(filename)

get_local_qps(ipnt)

to_pickle(filename)