Module: search

class kbmod.search.DebugTimer

Bases: pybind11_object

A simple timer used for consistent outputing of timing results in verbose mode. Timer automatically starts when it is created.

Parameters:

namestr

The name string of the timer. Used for ouput.

verbosebool

Output the timing information to the standard output.

Methods

read(self)	Read the timer duration as decimal seconds.
start(self)	Start (or restart) the timer.
stop(self)	Stop the timer.

read(self: kbmod.search.DebugTimer) → float

Read the timer duration as decimal seconds.

Returns:

durationfloat

The duration of the timer.

start(self: kbmod.search.DebugTimer) → None

Start (or restart) the timer. If verbose=True, the object outputs a message.

stop(self: kbmod.search.DebugTimer) → None

Stop the timer. If verbose=True, the object outputs the duration.

class kbmod.search.PsiPhi

Bases: pybind11_object

A named tuple for psi and phi values.

Attributes:

psifloat

The psi value at a pixel.

phifloat

The phi value at a pixel.

class kbmod.search.PsiPhiArray

Bases: pybind11_object

An encoded array of Psi and Phi values along with their meta data. This object supports automatic encoding of the psi/phi values (into float, uint8, or uint16) as well as the transfer of data to the GPU.

Attributes:

block_size

The size of a single entry in bytes.

cpu_array_allocated

A Boolean indicating whether the cpu data (psi/phi) array exists.

gpu_array_allocated

A Boolean indicating whether the gpu data (psi/phi) array exists.

height

The image height in pixels.

num_bytes

The target number of bytes to use for encoding the data (1 for uint8, 2 for uint16, or 4 for float32).

num_entries

The number of array entries (width x height x num_images).

num_times

The number of times.

on_gpu

A Boolean indicating whether a copy of the data is on the GPU.

phi_max_val

The maximum value of phi used in the scaling computations.

phi_min_val

The minimum value of phi used in the scaling computations.

phi_scale

The scaling parameter for phi.

pixels_per_image

The number of pixels per each image (width x height).

psi_max_val

The maximum value of psi used in the scaling computations.

psi_min_val

The minimum value of psi used in the scaling computations.

psi_scale

The scaling parameter for psi.

total_array_size

The size of the array in bytes.

width

The image width in pixels.

Methods

clear(self)	Clear all data and free the arrays.
clear_from_gpu(self)	Free the image and time data from the GPU memory.
move_to_gpu(self)	Move the image and time data to the GPU.
read_psi_phi(self, arg0, arg1, arg2)	Read a PsiPhi value from the CPU array.
read_time(self, arg0)	Read a zeroed time value from the CPU array.
set_meta_data(self, arg0, arg1, arg2, arg3)	Set the meta data for the array.
set_time_array(self, arg0)	Set the zeroed times.

property block_size

The size of a single entry in bytes.

clear(self: kbmod.search.PsiPhiArray) → None

Clear all data and free the arrays.

clear_from_gpu(self: kbmod.search.PsiPhiArray) → None

Free the image and time data from the GPU memory. Does not copy the data back to the CPU.

Raises:

Raises a RuntimeError the data or settings are invalid.

property cpu_array_allocated

A Boolean indicating whether the cpu data (psi/phi) array exists.

property gpu_array_allocated

A Boolean indicating whether the gpu data (psi/phi) array exists.

property height

The image height in pixels.

move_to_gpu(self: kbmod.search.PsiPhiArray) → None

Move the image and time data to the GPU. Allocates space and copies the data.

Raises:

Raises a RuntimeError if the data or settings are invalid.

property num_bytes

The target number of bytes to use for encoding the data (1 for uint8, 2 for uint16, or 4 for float32). Might differ from actual number of bytes (block_size).

property num_entries

The number of array entries (width x height x num_images).

property num_times

The number of times. Equivalent to the number of images stored.

property on_gpu

A Boolean indicating whether a copy of the data is on the GPU.

property phi_max_val

The maximum value of phi used in the scaling computations.

property phi_min_val

The minimum value of phi used in the scaling computations.

property phi_scale

The scaling parameter for phi.

property pixels_per_image

The number of pixels per each image (width x height).

property psi_max_val

The maximum value of psi used in the scaling computations.

property psi_min_val

The minimum value of psi used in the scaling computations.

property psi_scale

The scaling parameter for psi.

read_psi_phi(self: kbmod.search.PsiPhiArray, arg0: SupportsInt, arg1: SupportsInt, arg2: SupportsInt) → kbmod.search.PsiPhi

Read a PsiPhi value from the CPU array. Performs automatic decoding of compressed data and returns the value as a float.

Parameters:

timeint

The timestep to read.

rowint

The row in the image (y-dimension)

colint

The column in the image (x-dimension)

Returns:

PsiPhi

The pixel values.

read_time(self: kbmod.search.PsiPhiArray, arg0: SupportsInt) → float

Read a zeroed time value from the CPU array.

Parameters:

timeint

The timestep to read.

Returns:

float

The time.

set_meta_data(self: kbmod.search.PsiPhiArray, arg0: SupportsInt, arg1: SupportsInt, arg2: SupportsInt, arg3: SupportsInt) → None

Set the meta data for the array. Automatically called by fill_psi_phi_array().

Parameters:

new_num_bytesint

The type of encoding to use (1, 2, or 4).

new_num_timesint

The number of time steps in the data.

new_heightint

The height of each image in pixels.

new_widthint

The width of each image in pixels.

set_time_array(self: kbmod.search.PsiPhiArray, arg0: list[SupportsFloat]) → None

Set the zeroed times.

Parameters:

timeslist

A list of float with zeroed times.

property total_array_size

The size of the array in bytes.

property width

The image width in pixels.

class kbmod.search.StackSearch

Bases: pybind11_object

The data and configuration needed for KBMOD’s core search. It is created using either a reference to the ImageStack or lists of science, variance, and PSF information.

Parameters:

sci_imgslist

A list of science images as numpy arrays.

var_imgslist

A list of variance images as numpy arrays.

psf_kernelslist

A list of PSF kernels as numpy arrays.

zeroed_timeslist

A list of floating point times starting at zero.

num_bytesint

The number of bytes to use for encoding the data. This is used to set the encoding level for the data copied to the GPU. The default value is -1, which means no encoding is done. The other options are 1 (uint8), 2 (uint16), and 4 (float).

Attributes:

num_imagesint

The number of images (or time steps).

heightint

The height of each image in pixels.

widthint

The width of each image in pixels.

zeroed_timeslist

The times shift so the first time is at 0.0.

Methods

clear_results(self)	Clear the saved results.
compute_max_results(self)	Compute the maximum number of results according to the x, y bounds and the results per pixel.
disable_gpu_sigmag_filter(self)	Turns off the on-GPU sigma-G filtering.
enable_gpu_sigmag_filter(self, arg0, arg1, arg2)	Enable on-GPU sigma-G filtering.
evaluate_single_trajectory(self, arg0, arg1)	Performs the evaluation of a single Trajectory object.
get_all_psi_phi_curves(self, arg0)	Return a single matrix with both the psi and phi curves.
get_all_results(self)	Get a reference to the full list of results.
get_image_height(self)	Returns the height of the images in pixels.
get_image_width(self)	Returns the width of the images in pixels.
get_num_images(self)	Returns the number of images to process.
get_number_total_results(self)	Get the total number of saved results.
get_results(self, arg0, arg1)	Get a batch of cached results.
search_all(self, arg0, arg1)	Perform the KBMOD search by evaluating a list of candidate trajectories at each starting pixel in the image.
search_linear_trajectory(self, arg0, arg1, ...)	Performs the evaluation of a linear trajectory in pixel space.
set_min_lh(self, arg0)	Sets the minimum likelihood for valid result.
set_min_obs(self, arg0)	Sets the minimum number of observations for valid result.
set_results(self, arg0)	Set the cached results.
set_results_per_pixel(self, arg0)	Set the maximum number of results per pixel returns by a search.
set_start_bounds_x(self, arg0, arg1)	Set the starting and ending bounds in the x direction for a grid search.
set_start_bounds_y(self, arg0, arg1)	Set the starting and ending bounds in the y direction for a grid search.

clear_results(self: kbmod.search.StackSearch) → None

Clear the saved results.

compute_max_results(self: kbmod.search.StackSearch) → int

Compute the maximum number of results according to the x, y bounds and the results per pixel.

Returns:

max_resultsint

The maximum number of results that a search will return.

disable_gpu_sigmag_filter(self: kbmod.search.StackSearch) → None

Turns off the on-GPU sigma-G filtering.

enable_gpu_sigmag_filter(self: kbmod.search.StackSearch, arg0: list[SupportsFloat], arg1: SupportsFloat, arg2: SupportsFloat) → None

Enable on-GPU sigma-G filtering.

Parameters:

percentileslist

A length 2 list of percentiles (between 0.0 and 1.0). Example [0.25, 0.75].

sigmag_coefffloat

The sigma-G coefficient corresponding to the percentiles. This can be computed via SigmaGClipping.find_sigma_g_coeff().

min_lhfloat

The minimum likelihood for a result to be accepted.

Raises:

Raises a RunTimeError if invalid values are provided.

evaluate_single_trajectory(self: kbmod.search.StackSearch, arg0: search::Trajectory, arg1: bool) → None

Performs the evaluation of a single Trajectory object. Modifies the trajectory object in-place to add the statistics.

Parameters:

trjkb.Trajectory

The trjactory to evaluate.

use_kernelbool

Use the kernel code for evaluation. This requires the code is compiled with the nvidia libraries, but performs the exact same computations as on GPU.

Notes

Runs on the CPU.

get_all_psi_phi_curves(self: kbmod.search.StackSearch, arg0: list[search::Trajectory]) → Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]']

Return a single matrix with both the psi and phi curves. Each row corresponds to a single trajectory and the columns hold the psi values then the phi values (in order of time).

Parameters:

trjlist of kb.Trajectory

The input trajectories.

Returns:

resultnp.ndarray

A shape (R, 2T) matrix where R is the number of trajectories and T is the number of time steps. The first T columns contain the psi values and the second T columns contain the phi columns.

get_all_results(self: kbmod.search.StackSearch) → list[search::Trajectory]

Get a reference to the full list of results.

Returns:

resultsList

A list of Trajectory objects for the cached results.

get_image_height(self: kbmod.search.StackSearch) → int

Returns the height of the images in pixels.

get_image_width(self: kbmod.search.StackSearch) → int

Returns the width of the images in pixels.

get_num_images(self: kbmod.search.StackSearch) → int

Returns the number of images to process.

get_number_total_results(self: kbmod.search.StackSearch) → int

Get the total number of saved results.

Returns:

resultint

The number of saved results.

get_results(self: kbmod.search.StackSearch, arg0: SupportsInt, arg1: SupportsInt) → list[search::Trajectory]

Get a batch of cached results.

Parameters:

startint

The starting index of the results to retrieve. Returns an empty list if start is past the end of the cache.

countint

The maximum number of results to retrieve. Returns fewer results if there are not enough in the cache.

Returns:

resultsList

A list of Trajectory objects for the cached results.

Raises:

RunTimeError if start < 0 or count <= 0.

search_all(self: kbmod.search.StackSearch, arg0: list[search::Trajectory], arg1: bool) → None

Perform the KBMOD search by evaluating a list of candidate trajectories at each starting pixel in the image. The results are stored in the StackSearch object and can be accessed with get_results().

Parameters:

search_listlist

A list of Trajectory objects where each trajectory is evaluated at each starting pixel.

on_gpubool

Run the search on the GPU.

search_linear_trajectory(self: kbmod.search.StackSearch, arg0: SupportsInt, arg1: SupportsInt, arg2: SupportsFloat, arg3: SupportsFloat, arg4: bool) → search::Trajectory

Performs the evaluation of a linear trajectory in pixel space.

Parameters:

xshort

The starting x pixel of the trajectory.

yshort

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

Use the kernel code for evaluation. This requires the code is compiled with the nvidia libraries, but performs the exact same computations as on GPU.

Returns:

resultkb.Trajectory

The trajectory object with statistics set.

Notes

Runs on the CPU, but requires CUDA compiler.

set_min_lh(self: kbmod.search.StackSearch, arg0: SupportsFloat) → None

Sets the minimum likelihood for valid result.

Parameters:

new_valuefloat

The minimum likelihood value for a trajectory to be returned.

set_min_obs(self: kbmod.search.StackSearch, arg0: SupportsInt) → None

Sets the minimum number of observations for valid result.

Parameters:

new_valueint

The minimum number of valid observations for a trajectory to be returned.

set_results(self: kbmod.search.StackSearch, arg0: list[search::Trajectory]) → None

Set the cached results. Used for testing.

Parameters:

new_resultsList

The list of results (Trajectory objects) to store.

set_results_per_pixel(self: kbmod.search.StackSearch, arg0: SupportsInt) → None

Set the maximum number of results per pixel returns by a search.

Parameters:

new_valueint

The new number of results per pixel.

Raises:

Raises a RunTimeError if an invalid value is provided (new_value <= 0).

set_start_bounds_x(self: kbmod.search.StackSearch, arg0: SupportsInt, arg1: SupportsInt) → None

Set the starting and ending bounds in the x direction for a grid search. The grid search will test all pixels [x_min, x_max).

Parameters:

x_minint

The inclusive lower bound of the search (in pixels).

x_maxint

The exclusive upper bound of the search (in pixels).

Raises:

Raises a RunTimeError if invalid bounds are provided (x_max > x_min).

set_start_bounds_y(self: kbmod.search.StackSearch, arg0: SupportsInt, arg1: SupportsInt) → None

Set the starting and ending bounds in the y direction for a grid search. The grid search will test all pixels [y_min, y_max).

Parameters:

y_minint

The inclusive lower bound of the search (in pixels).

y_maxint

The exclusive upper bound of the search (in pixels).

Raises:

Raises a RunTimeError if invalid bounds are provided (x_max > x_min).

class kbmod.search.StampType

Bases: pybind11_object

Members:

STAMP_SUM

STAMP_MEAN

STAMP_MEDIAN

STAMP_VAR_WEIGHTED

Attributes:

name

name(self: object, /) -> str

value

property name

class kbmod.search.Trajectory

Bases: pybind11_object

A structure for holding basic information about potential results in the form of a linear trajectory in pixel space.

Attributes:

xfloat

x coordinate of the trajectory at first time step (in pixels)

yfloat

y coordinate of the trajectory at first time step (in pixels)

vxfloat

x component of the velocity, as projected on the image (in pixels per day)

vyfloat

y component of the velocity, as projected on the image (in pixels per day)

lhfloat

The computed likelihood of all (valid) points along the trajectory.

fluxfloat

The computed likelihood of all (valid) points along the trajectory.

obs_countint

The number of valid points along the trajectory.

Methods

get_x_index(self, arg0)	Returns the predicted x position of the trajectory as an integer (column) index.
get_x_pos(self, time[, centered])	Returns the predicted x position of the trajectory.
get_y_index(self, arg0)	Returns the predicted x position of the trajectory as an integer (row) index.
get_y_pos(self, time[, centered])	Returns the predicted y position of the trajectory.

get_x_index(self: kbmod.search.Trajectory, arg0: SupportsFloat) → int

Returns the predicted x position of the trajectory as an integer (column) index.

Parameters:

timefloat

A zero shifted time in days.

Returns:

int

The predicted column index.

get_x_pos(self: kbmod.search.Trajectory, time: SupportsFloat, centered: bool = True) → float

Returns the predicted x position of the trajectory.

Parameters:

timefloat

A zero shifted time in days.

centeredbool

Shift the prediction to be at the center of the pixel (e.g. xp = x + vx * time + 0.5f). Default = True.

Returns:

float

The predicted x position (in pixels).

get_y_index(self: kbmod.search.Trajectory, arg0: SupportsFloat) → int

Returns the predicted x position of the trajectory as an integer (row) index.

Parameters:

timefloat

A zero shifted time in days.

Returns:

int

The predicted row index.

get_y_pos(self: kbmod.search.Trajectory, time: SupportsFloat, centered: bool = True) → float

Returns the predicted y position of the trajectory.

Parameters:

timefloat

A zero shifted time in days.

centeredbool

Shift the prediction to be at the center of the pixel (e.g. xp = x + vx * time + 0.5f). Default = True.

Returns:

float

The predicted y position (in pixels).

class kbmod.search.TrajectoryList

Bases: pybind11_object

A list of trajectories that can be transferred between CPU or GPU.

Attributes:

on_gpu

Whether the data currently resides on the GPU (True) or CPU (False)

Methods

estimate_memory(self)	Estimate the size of the list in bytes.
filter_by_likelihood(self, arg0)	Filter all trajectories with a likelihood above the given threshold.
filter_by_obs_count(self, arg0)	Filter all trajectories with an obs_count above the given threshold.
get_batch(self, arg0, arg1)	Return a batch of results.
get_list(self)	Return the full list of trajectories.
get_memory(self)	Return the size of the list in bytes.
get_size(self)	Return the size of the list in number of elements.
get_trajectory(self, arg0)	Get a reference trajectory from the list.
move_to_cpu(self)	Move the data from GPU to CPU.
move_to_gpu(self)	Move the data from CPU to GPU.
resize(self, arg0)	Forcibly resize the array.
set_trajectories(self, arg0)	Set an entire list of trajectories.
set_trajectory(self, arg0, arg1)	Set a trajectory in the list.
sort_by_likelihood(self)	Sort the data in order of decreasing likelihood.

estimate_memory(self: SupportsInt) → int

Estimate the size of the list in bytes.

Parameters:

num_elementsint

The number of elements that will be in the list.

Returns:

sizeint

The number of bytes needed for the list on CPU and GPU.

filter_by_likelihood(self: kbmod.search.TrajectoryList, arg0: SupportsFloat) → None

Filter all trajectories with a likelihood above the given threshold. Changes the order of the data and the size of the list. The data must reside on the CPU.

Parameters:

min_lhfloat

The minimum likelihood.

Raises:

Raises a RuntimeError the data is on GPU.

filter_by_obs_count(self: kbmod.search.TrajectoryList, arg0: SupportsInt) → None

Filter all trajectories with an obs_count above the given threshold. Changes the order of the data and the size of the list. The data must reside on the CPU.

Parameters:

min_obs_countint

The minimum obs_count.

Raises:

Raises a RuntimeError the data is on GPU.

get_batch(self: kbmod.search.TrajectoryList, arg0: SupportsInt, arg1: SupportsInt) → list[kbmod.search.Trajectory]

Return a batch of results. The data must reside on the CPU.

Parameters:

startint

The starting index of the results to retrieve. Returns an empty list if start is past the end of the cache.

countint

The maximum number of results to retrieve. Returns fewer results if there are not enough in the cache.

Returns:

resultsList

A list of Trajectory objects for the cached results.

Raises:

RunTimeError if start < 0 or count <= 0 or if the data is on GPU.

get_list(self: kbmod.search.TrajectoryList) → list[kbmod.search.Trajectory]

Return the full list of trajectories. The data must reside on the CPU.

Returns:

resultlist

The list of trajectories

Raises:

Raises a RuntimeError if the data currently resides on the GPU.

get_memory(self: kbmod.search.TrajectoryList) → int

Return the size of the list in bytes.

get_size(self: kbmod.search.TrajectoryList) → int

Return the size of the list in number of elements.

get_trajectory(self: kbmod.search.TrajectoryList, arg0: SupportsInt) → kbmod.search.Trajectory

Get a reference trajectory from the list. The data must reside on the CPU.

Parameters:

indexint

The index of the entry.

Returns:

trjTrajectory

The corresponding Trajectory object.

Raises:

Raises a RuntimeError if the index is invalid or the data currently resides

on the GPU.

move_to_cpu(self: kbmod.search.TrajectoryList) → None

Move the data from GPU to CPU. If the data is already on the CPU this is a no-op.

Raises:

Raises a RuntimeError if invalid state encountered.

move_to_gpu(self: kbmod.search.TrajectoryList) → None

Move the data from CPU to GPU. If the data is already on the GPU this is a no-op.

Raises:

Raises a RuntimeError if invalid state encountered.

property on_gpu

Whether the data currently resides on the GPU (True) or CPU (False)

resize(self: kbmod.search.TrajectoryList, arg0: SupportsInt) → None

Forcibly resize the array. If the size is decreased, the extra entries are dropped from the back. If the size is increased, extra (blank) trajectories are added to the back.

The data must reside on the CPU.

Parameters:

new_sizeint

The new size of the list.

Raises:

RunTimeError if new_size < 0 or data is on GPU.

set_trajectories(self: kbmod.search.TrajectoryList, arg0: list[kbmod.search.Trajectory]) → None

Set an entire list of trajectories. Resizes the array to match the given input. The data must reside on the CPU.

Parameters:

new_valueslist

A list of Trajectory objects.

Raises:

Raises a RuntimeError if the index is invalid or the data currently resides

on the GPU.

set_trajectory(self: kbmod.search.TrajectoryList, arg0: SupportsInt, arg1: kbmod.search.Trajectory) → None

Set a trajectory in the list. The data must reside on the CPU.

Parameters:

indexint

The index of the entry.

new_valueTrajectory

The corresponding Trajectory object.

Raises:

Raises a RuntimeError if the index is invalid or the data currently resides

on the GPU.

sort_by_likelihood(self: kbmod.search.TrajectoryList) → None

Sort the data in order of decreasing likelihood. The data must reside on the CPU.

Raises:

Raises a RuntimeError the data is on GPU.

kbmod.search.compute_scale_params_from_image_vect(arg0: list[Annotated[numpy.typing.ArrayLike, numpy.float32, '[m, n]']], arg1: SupportsInt) → Annotated[list[float], FixedSize(3)]

kbmod.search.convolve_image(image: Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]'], psf: Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]']) → Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]']

Convolves the image (in place) with a PSF using a CPU if one is available and a GPU otherwise.

Parameters:

imagenumpy.ndarray

The image data as a two dimensional array.

psfnumpy.ndarray

The kernel of the Point Spread Function as a two dimensional array.

Returns:

resultnumpy.ndarray

The resulting image.

kbmod.search.convolve_image_cpu(image: Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]'], psf: Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]']) → Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]']

Convolve the image with a PSF on the CPU.

Parameters:

imagenumpy.ndarray

The image data as a two dimensional array.

psfnumpy.ndarray

The kernel of the Point Spread Function as a two dimensional array.

Returns:

resultnumpy.ndarray

The resulting image.

kbmod.search.convolve_image_gpu(image: Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]'], psf: Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]']) → Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]']

Convolve the image with a PSF on the GPU.

Parameters:

imagenumpy.ndarray

The image data as a two dimensional array.

psfnumpy.ndarray

The kernel of the Point Spread Function as a two dimensional array.

Returns:

resultnumpy.ndarray

The resulting image.

kbmod.search.decode_uint_scalar(arg0: SupportsFloat, arg1: SupportsFloat, arg2: SupportsFloat) → float

kbmod.search.encode_uint_scalar(arg0: SupportsFloat, arg1: SupportsFloat, arg2: SupportsFloat, arg3: SupportsFloat) → float

kbmod.search.evaluate_trajectory_cpu(arg0: search::PsiPhiArray, arg1: search::Trajectory) → None

kbmod.search.extract_all_trajectory_flux(arg0: list[kbmod.search.Trajectory]) → list[float]

Extract all the flux values from a list of trajectories.

Parameters:

trjslist of Trajectory

The trajectories to process.

Returns:

resultslist of float

The flux values for each trajectory in the list.

kbmod.search.extract_all_trajectory_lh(arg0: list[kbmod.search.Trajectory]) → list[float]

Extract all the likelihood values from a list of trajectories.

Parameters:

trjslist of Trajectory

The trajectories to process.

Returns:

resultslist of float

The likelihood values for each trajectory in the list.

kbmod.search.extract_all_trajectory_obs_count(arg0: list[kbmod.search.Trajectory]) → list[int]

Extract all the vy values from a list of trajectories.

Parameters:

trjslist of Trajectory

The trajectories to process.

Returns:

resultslist of int

The obs_count values for each trajectory in the list.

kbmod.search.extract_all_trajectory_vx(arg0: list[kbmod.search.Trajectory]) → list[float]

Extract all the vx values from a list of trajectories.

Parameters:

trjslist of Trajectory

The trajectories to process.

Returns:

resultslist of float

The vx values for each trajectory in the list.

kbmod.search.extract_all_trajectory_vy(arg0: list[kbmod.search.Trajectory]) → list[float]

Extract all the vy values from a list of trajectories.

Parameters:

trjslist of Trajectory

The trajectories to process.

Returns:

resultslist of float

The vy values for each trajectory in the list.

kbmod.search.extract_all_trajectory_x(arg0: list[kbmod.search.Trajectory]) → list[int]

Extract all the x values from a list of trajectories.

Parameters:

trjslist of Trajectory

The trajectories to process.

Returns:

resultslist of int

The x values for each trajectory in the list.

kbmod.search.extract_all_trajectory_y(arg0: list[kbmod.search.Trajectory]) → list[int]

Extract all the y values from a list of trajectories.

Parameters:

trjslist of Trajectory

The trajectories to process.

Returns:

resultslist of int

The y values for each trajectory in the list.

kbmod.search.fill_psi_phi_array(arg0: kbmod.search.PsiPhiArray, arg1: SupportsInt, arg2: list[Annotated[numpy.typing.ArrayLike, numpy.float32, '[m, n]']], arg3: list[Annotated[numpy.typing.ArrayLike, numpy.float32, '[m, n]']], arg4: list[SupportsFloat]) → None

Fill the PsiPhiArray from Psi and Phi images.

Parameters:

result_dataPsiPhiArray

The location to store the data.

num_bytesint

The type of encoding to use (1, 2, or 4).

psi_imgslist

A list of psi images as numpy arrays.

phi_imgslist

A list of phi images as numpy arrays.

zeroed_timeslist

A list of floating point times starting at zero.

Raises:

Raises a RuntimeError if invalid values are found in the psi or phi arrays.

kbmod.search.fill_psi_phi_array_from_image_arrays(arg0: kbmod.search.PsiPhiArray, arg1: SupportsInt, arg2: list[Annotated[numpy.typing.ArrayLike, numpy.float32, '[m, n]']], arg3: list[Annotated[numpy.typing.ArrayLike, numpy.float32, '[m, n]']], arg4: list[Annotated[numpy.typing.ArrayLike, numpy.float32, '[m, n]']], arg5: list[SupportsFloat]) → None

Fill the PsiPhiArray from arrays of the image data.

Parameters:

result_dataPsiPhiArray

The location to store the data.

num_bytesint

The type of encoding to use (1, 2, or 4).

sci_imgslist

A list of science images as numpy arrays.

var_imgslist

A list of variance images as numpy arrays.

psf_kernelslist

A list of PSF kernels as numpy arrays.

zeroed_timeslist

A list of floating point times starting at zero.

Raises:

Raises a RuntimeError if invalid values are found.

kbmod.search.generate_phi(var: Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]'], psf: Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]']) → Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]']

Generates the full psi image where the value of each pixel p in the resulting image is 1.0 / variance[p], skipping masked pixels.

Convolves the resulting image with the square of the PSF.

Parameters:

varnumpy.ndarray

The variance data as a H x W dimensional array.

psfnumpy.ndarray

The kernel of the Point Spread Function as a two dimensional array.

Returns:

resultnumpy.ndarray

A numpy array the same shape as the input image.

kbmod.search.generate_psi(sci: Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]'], var: Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]'], psf: Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]']) → Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]']

Generates the full psi image where the value of each pixel p in the resulting image is science[p] / variance[p], skipping masked pixels. Convolves the resulting image with the PSF.

Parameters:

scinumpy.ndarray

The science data as a H x W dimensional array.

varnumpy.ndarray

The variance data as a H x W dimensional array.

psfnumpy.ndarray

The kernel of the Point Spread Function as a two dimensional array.

Returns:

resultnumpy.ndarray

A numpy array the same shape as the input image.

kbmod.search.get_gpu_free_memory() → int

Return the GPUs free memory in bytes.

kbmod.search.get_gpu_total_memory() → int

Return the GPUs total memory in bytes.

kbmod.search.kb_has_gpu() → bool

Check if GPU is available

kbmod.search.pixel_value_valid(arg0: SupportsFloat) → bool

kbmod.search.print_cuda_stats() → None

Display the basic GPU information to standard out.

kbmod.search.search_cpu_only(arg0: search::PsiPhiArray, arg1: search::SearchParameters, arg2: search::TrajectoryList, arg3: search::TrajectoryList) → None

kbmod.search.sigmag_filtered_indices(arg0: list[SupportsFloat], arg1: SupportsFloat, arg2: SupportsFloat, arg3: SupportsFloat, arg4: SupportsFloat) → list[int]

kbmod.search.square_psf_values(given_psf: Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]']) → Annotated[numpy.typing.NDArray[numpy.float32], '[m, n]']

Compute the (unnormalized) square of a psf. This squares each value in the PSF kernel.

Parameters:

given_psfnumpy.ndarray

The kernel of the Point Spread Function as a two dimensional array.

Returns:

resultnumpy.ndarray

The resulting kernel.

kbmod.search.stat_gpu_memory_mb() → str

Create a minimal GPU stats string for debugging.

kbmod.search.validate_gpu(req_memory: SupportsInt = 0) → bool

Check that a GPU is present, accessible, and has sufficient memory.

Parameters:

req_memoryint

The minimum free memory in bytes. Default: 0

Returns:

bool

Indicates whether the GPU is valid.