Module: trajectory_explorer

class kbmod.trajectory_explorer.TrajectoryExplorer(im_stack, config=None, preload_data=False)

A class to interactively run test trajectories through KBMOD.

Attributes:

clipperSigmaGClipping

The sigma-G clipping object.

configSearchConfiguration

The configuration parameters.

im_stackImageStackPy

The images to search.

preload_databool

If True preload all the image data into memory.

searchkb.StackSearch

The search object (with cached data).

Methods

apply_sigma_g(result)	Apply sigma G clipping to a single ResultRow.
evaluate_angle_trajectory(ra, dec, v_ra, ...)	Evaluate a single linear trajectory in angle space.
evaluate_around_linear_trajectory(x, y, vx, vy)	Evaluate all the trajectories within a local neighborhood of the given trajectory.
evaluate_linear_trajectory(x, y, vx, vy, ...)	Evaluate a single linear trajectory in pixel space.
initialize_data([config])	Initialize the data, including applying the configuration parameters.
refine_linear_trajectory(x, y, vx, vy, *[, ...])	Evaluate all trajectories within a local neighborhood of the given trajectory (applying standard filtering) and return the best one.

apply_sigma_g(result)

Apply sigma G clipping to a single ResultRow. Modifies the row in-place.

Parameters:

resultResults

A table of results to test.

evaluate_angle_trajectory(ra, dec, v_ra, v_dec, wcs, use_kernel)

Evaluate a single linear trajectory in angle space. Skips all the filtering steps and returns the raw data.

Parameters:

rafloat

The right ascension at time t0 (in degrees)

decfloat

The declination at time t0 (in degrees)

v_rafloat

The velocity in RA at t0 (in degrees/day)

v_decfloat

The velocity in declination at t0 (in degrees/day)

wcsastropy.wcs.WCS

The WCS for the images.

use_kernelbool

Force the use of the exact kernel code (including on GPU-sigma G).

Returns:

resultResults

The results table with a single row and all the columns filled out.

evaluate_around_linear_trajectory(x, y, vx, vy, pixel_radius=5, max_ang_offset=0.2618, ang_step=0.035, max_vel_offset=10.0, vel_step=0.5, use_gpu=True)

Evaluate all the trajectories within a local neighborhood of the given trajectory. No filtering is done at all.

Parameters:

xint

The starting x pixel of the trajectory.

yint

The starting y pixel of the trajectory.

vxfloat

The x velocity of the trajectory in pixels per day.

vyfloat

The y velocity of the trajectory in pixels per day.

pixel_radiusint

The number of pixels to evaluate to each side of the Trajectory’s starting pixel.

max_ang_offsetfloat

The maximum offset of a candidate trajectory from the original (in radians)

ang_stepfloat

The step size to explore for each angle (in radians)

max_vel_offsetfloat

The maximum offset of the velocity’s magnitude from the original (in pixels per day)

vel_stepfloat

The step size to explore for each velocity magnitude (in pixels per day)

use_gpubool

Run the search on GPU.

Returns:

resultResults

The results table with a single row and all the columns filled out.

evaluate_linear_trajectory(x, y, vx, vy, use_kernel)

Evaluate a single linear trajectory in pixel space. Skips all the filtering steps and returns the raw data.

Parameters:

xint

The starting x pixel of the trajectory.

yint

The starting y pixel of the trajectory.

vxfloat

The x velocity of the trajectory in pixels per day.

vyfloat

The y velocity of the trajectory in pixels per day.

use_kernelbool

Force the use of the exact kernel code (including on GPU-sigma G).

Returns:

resultResults

The results table with a single row and all the columns filled out.

initialize_data(config=None)

Initialize the data, including applying the configuration parameters.

Parameters:

configSearchConfiguration, optional

Any custom configuration parameters to use for this run. If None uses the default configuration parameters.

refine_linear_trajectory(x, y, vx, vy, *, pixel_radius=50, max_dv=10.0, dv_steps=21, max_results=1, use_gpu=True)

Evaluate all trajectories within a local neighborhood of the given trajectory (applying standard filtering) and return the best one.

Parameters:

xint

The starting x pixel of the trajectory.

yint

The starting y pixel of the trajectory.

vxfloat

The x velocity of the trajectory in pixels per day.

vyfloat

The y velocity of the trajectory in pixels per day.

pixel_radiusint

The number of pixels to evaluate to each side of the Trajectory’s starting pixel.

max_dvfloat

The maximum change in per-coordinate pixel velocity to explore (in pixels/day)

dv_steps: `int`

The number of steps to explore in each velocity dimension.

max_resultsint

The maximum number of results to return.

use_gpubool

Run the search on GPU.

Returns:

resultResults

The results table with a single row and all the columns filled out.

kbmod.trajectory_explorer.refine_all_results(results, im_stack, config, *, deduplicate=True, pixel_radius=50, max_dv=10.0, dv_steps=21)

Refine the trajectories in results by re-running the search in a small neighborhood around each trajectory.

Parameters:

resultsResults

The current table of results including the per-pixel trajectories. This is modified in-place.

im_stackImageStackPy

The stack of image data.

configSearchConfiguration

The configuration parameters

deduplicatebool, optional

If True, remove duplicate trajectories after refinement (this includes any trajectories whose start and end points are within the pixel radius).

pixel_radiusint

The number of pixels to evaluate to each side of the Trajectory’s starting pixel.

max_dvfloat

The maximum change in per-coordinate pixel velocity to explore (in pixels/day)

dv_steps: `int`

The number of steps to explore in each velocity dimension.

Returns:

refinedResults

A new Results object with the refined trajectories.