csaxs_bec.scans.owis_grid.OwisGrid#

class OwisGrid(start_y: float, end_y: float, interval_y: int, start_x: float, end_x: float, interval_x: int, *args, exp_time: float = 0.1, readout_time: float = 0.003, **kwargs)[source]#

Bases: AsyncFlyScanBase

Owis-based grid scan.

Parameters:

start_y (float) – start position of y axis (fast axis)
end_y (float) – end position of y axis (fast axis)
interval_y (int) – number of points in y axis
start_x (float) – start position of x axis (slow axis)
end_x (float) – end position of x axis (slow axis)
interval_x (int) – number of points in x axis
exp_time (float) – exposure time in seconds. Default is 0.1s
readout_time (float) – readout time in seconds, minimum of 3e-3s (3ms)

Exp:: scans.sgalil_grid(start_y = val1, end_y= val1, interval_y = val1, start_x = val1, end_x = val1, interval_x = val1, exp_time = 0.02, readout_time = 3e-3)

Methods

`analyze_path_quality`	Analyze the quality of a path by looking at the distribution of step sizes.
`cleanup`	call the cleanup procedure
`close_scan`	close the scan
`compute_scan_params`	Compute scan parameters.
`device_msg_metadata`
`finalize`	Finalize scan, set motor speed and acceleration to initial values
`get_initial_motor_properties`
`get_path_length`	Calculate the total length of a path defined by a sequence of positions.
`get_radius`	Calculate the radial distance from the origin for each position
`initialize`
`move_to_start`	return to the start position
`open_scan`	open the scan
`optimize_corridor`	Optimize positions using a corridor-based approach.
`optimize_nearest_neighbor`	Optimize path by always moving to the nearest unvisited point.
`optimize_shell`	Optimize a path through a set of positions by sorting them in concentric shells.
`pre_scan`	Pre scan instructions, move to start position
`prepare_positions`	prepare the positions for the scan
`read_scan_motors`	read the scan motors
`run`	run the scan.
`run_baseline_reading`	perform a reading of all baseline devices
`run_pre_scan_macros`	run pre scan macros if any
`scan`
`scan_core`	This is the main event loop.
`scan_progress`	Timeout of the progress bar.
`scan_report_instructions`	Scan report instructions for the progress bar, yields from mcs card
`stage`	call the stage procedure
`unstage`	call the unstage procedure
`update_readout_priority`	update the readout priority for this request.
`update_scan_motors`	Scan motors are automatically elevated to readout priority monitored and read out in the beginning of the scan.

Attributes

`arg_bundle_size`
`arg_input`
`gui_args`
`monitor_sync`	monitor_sync is the flyer that will be used to synchronize the monitor readings in the scan bundler.
`pre_move`
`required_kwargs`
`return_to_start_after_abort`
`scan_name`
`scan_report_devices`	devices to be included in the scan report
`scan_report_hint`
`scan_type`
`use_scan_progress_report`

analyze_path_quality(pos: ndarray) → PathQualityStats#

Analyze the quality of a path by looking at the distribution of step sizes.

Parameters:: pos (np.ndarray) – Array of positions
Returns:: Dictionary with statistics about the path quality
Return type:: dict

cleanup()#: call the cleanup procedure

close_scan()#: close the scan

compute_scan_params()[source]#: Compute scan parameters. This includes the velocity, acceleration and premove distance.

finalize()[source]#: Finalize scan, set motor speed and acceleration to initial values

get_path_length(pos: ndarray) → float#

Calculate the total length of a path defined by a sequence of positions. :param pos: Array of positions :type pos: np.ndarray

Returns:: Total path length
Return type:: float

get_radius(pos: ndarray) → ndarray#

Calculate the radial distance from the origin for each position

Parameters:: pos (np.ndarray) – Array of positions
Returns:: Radial distances
Return type:: np.ndarray

property monitor_sync#: monitor_sync is the flyer that will be used to synchronize the monitor readings in the scan bundler. The return value should be the name of the flyer device.

move_to_start()#: return to the start position

open_scan()#: open the scan

optimize_corridor(positions: ndarray, corridor_size: float | None = None, sort_axis: int = 1, num_iterations: int = 1, preferred_direction: int | None = None, corridor_estimation: Literal['density', 'median_distance'] = 'median_distance')#

Optimize positions using a corridor-based approach.

Note: This method is designed for 2D positions. If higher dimensions are provided, the positions are returned unmodified.

Parameters:

positions (np.ndarray) – Array of positions
corridor_size (float, optional) – Width of each corridor. Defaults to None (auto-estimated).
sort_axis (int, optional) – Axis along which to create corridors (0 or 1). Defaults to 1.
num_iterations (int, optional) – Number of corridor sizes to try. Defaults to 1.
preferred_direction (int | None, optional) – Preferred direction for the primary axis (1 or -1). If None, alternates direction for each corridor.
corridor_estimation (str, optional) – Method for estimating corridor size if not provided. Options are “density” or “median_distance”. Defaults to “density”.

Returns:

Optimized positions

Return type:

np.ndarray

optimize_nearest_neighbor(positions: ndarray, start_index: int | None = None) → ndarray#

Optimize path by always moving to the nearest unvisited point.

This is a greedy algorithm that provides good results for many scenarios with relatively low computational complexity.

Parameters:

positions (np.ndarray) – Array of positions
start_index (int | None, optional) – Index of the starting point. If None, the algorithm will start from the point closest to the origin.

Returns:

Optimized positions

Return type:

np.ndarray

optimize_shell(pos: ndarray, offset: float | None = None, dr: float | None = None, num_iterations: int = 3)#

Optimize a path through a set of positions by sorting them in concentric shells.

Parameters:

pos (np.ndarray) – Array of positions
offset (float, optional) – Offset for the first shell. Defaults to None (auto-estimated).
dr (float, optional) – Width of each shell. Defaults to None (auto-estimated).
num_iterations (int, optional) – Number of parameter variations to try. Defaults to 3.

Returns:

Optimized positions

Return type:

np.ndarray

pre_scan()[source]#: Pre scan instructions, move to start position

prepare_positions()#: prepare the positions for the scan

read_scan_motors()#: read the scan motors

run()#: run the scan. This method is called by the scan server and is the main entry point for the scan.

run_baseline_reading()#: perform a reading of all baseline devices

run_pre_scan_macros()#: run pre scan macros if any

scan_core()[source]#: This is the main event loop.

scan_progress() → int[source]#: Timeout of the progress bar. This gets updated in the frequency of scan segments

property scan_report_devices#: devices to be included in the scan report

scan_report_instructions()[source]#: Scan report instructions for the progress bar, yields from mcs card

stage()#: call the stage procedure

unstage()#: call the unstage procedure

update_readout_priority()#: update the readout priority for this request. Typically the monitored devices should also include the scan motors.

update_scan_motors() → None#: Scan motors are automatically elevated to readout priority monitored and read out in the beginning of the scan.