pyfast_adt.main.adaptor.camera.adaptor_haadf

Attributes

cam

Classes

Cam_haadf

Software interface for the F30 Fischione HAADF detector passing through temscript interface.

Module Contents

class pyfast_adt.main.adaptor.camera.adaptor_haadf.Cam_haadf(cam_table=None, instance_gui=None)

Bases: pyfast_adt.main.adaptor.camera.adaptor_cam.Cam_base

Software interface for the F30 Fischione HAADF detector passing through temscript interface.

cam_table = None

name = None

exposure = None

x = None

y = None

processing = None

delay = None

binning = None

buffer_size = None

buffer = None

stop_signal = None

table = None

instance_gui = None

client

connect()

Connect to the camera.

initialize_detector()

release_connection()

‘ release the connection with the device

get_binning()

in temscript binning control the Max frame parameter in the STEM Imaging Expert flap, so no magnification scaling is applied, like a conventional CCD camera

set_binning(binning: int)

in temscript binning control the Max frame parameter in the STEM Imaging Expert flap, so no magnification scaling is applied, like a conventional CCD camera

get_image_size()

in temscript image_size control the frame size parameter in the STEM Imaging Expert flap, so a magnification scaling is applied because a smaller area is scanned. from temscript is possible from a 2k**2 haadf to reach a 512**2 haadf maximum.

image_size == “FULL” (2k), “HALF” (1k), “QUARTER” (512)

set_image_size(image_size: str)

image_size == “FULL”(2k), “HALF”(1k), “QUARTER”(512)

set_processing(processing: str)

‘ set the processing of the camera, processing = “Unprocessed, Background subtracted, Gain normalized”

get_processing()

‘ get the processing typeof the camera

acquire_image(binning: int, exposure_time: int, image_size: str)

Acquire image through its adaptor and return it as np.array.

acquire_image_and_show(binning: int, exposure_time: int, image_size: str)

acquire_image_fast()

rotate_img(img, times=None, flip_h=None, flip_v=None, flip_diag=None)

stop_liveview() → None

‘ stop the live view of the camera

start_liveview(delay: float = 3.0) → None

‘ start the live view of the camera

set_exposure(exposure_time: int) → None

Set exposure time in us.

get_exposure() → int

Return exposure time in ms.

acquire_series_images(exposure_time: int, binning: int, processing: str, buffer_size: int, stop_signal, display=False)

prepare_acquisition_cRED_data(camera, binning, exposure, image_size, buffer_size, FPS_devider=1)

“ camera = spirit_haadf, binning = one of the available binning for the choosen camera, exposure = choosen exposure, buffer_size = dimension of the stack where saving the output images

acquisition_cRED_data(stage_thread=None, timer=None, event=None, stop_event=None)

Acquire images into the buffer up to the thread is alive, usually the stage thread is passed for cRED experiments

save_cRED_data(savingpath)

get_camera_characteristic()

load_calibration_table()

is_cam_streaming()

True is the camera have a live mode where you can retrieve the images from the memory like the xf416r, otherwise False like the timepix1

is_cam_bottom_mounted()

True if the camera is mounted on the bottom of the microscope, otherwise False

get_stem_beamshift()

set_stem_beamshift(beamshift)

beamshift need to be a tuple of x,y in meters

pyfast_adt.main.adaptor.camera.adaptor_haadf.cam