pyfast_adt.main.adaptor.camera.adaptor_timepix1
Attributes
Classes
Software interface for the ASI timepix1 camera. |
Functions
Module Contents
- pyfast_adt.main.adaptor.camera.adaptor_timepix1.path1 = 'adaptor/camera/timepix1/Libraries/Tpx_Controller_Mainz.dll'
- pyfast_adt.main.adaptor.camera.adaptor_timepix1.setup_imported_methods()
- class pyfast_adt.main.adaptor.camera.adaptor_timepix1.Cam_timepix1(_id, instance_gui=None)
Bases:
pyfast_adt.main.adaptor.camera.adaptor_cam.Cam_baseSoftware interface for the ASI timepix1 camera.
- drc_namestr
Set the default folder to store data in
- namestr
Name of the interface
- name = None
- _id
- exposure = 100
- x = None
- y = None
- processing = None
- delay = None
- binning = 1
- buffer_size = None
- stop_signal = None
- buffer = None
- instance_gui = None
- table = None
- timings = []
- connect()
‘ connection with the device
- release_connection()
‘ release the connection with the device
- set_exposure(exposure_time: int)
‘ set the exposure time in ms for the camera
- get_exposure()
‘ get the exposure time in ms for the camera
- start_liveview(delay: float)
‘ start the live view of the camera
- stop_liveview()
‘ stop the live view of the camera
- set_binning(binning: int)
‘ set the binning of the camera, common parameters are 1, 2, 4, 8
- get_binning()
‘ get the binning of the camera
- acquire_image(exposure_time: int, binning: int, processing='Unprocessed')
Acquire image through its adaptor and return it
- acquire_image_and_show(exposure_time: int, binning: int, processing='Unprocessed')
Acquire image through its adaptor and return it
- rotate_img(img, times=None, flip_h=None, flip_v=None)
- 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_series_images(exposure_time: int, binning: int, processing: str, buffer_size: int, stop_signal, display=False)
- prepare_acquisition_cRED_data(camera: str, binning: int, exposure: int, buffer_size, FPS_devider=1)
- 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)
- grab_image_from_detector()
collect an image from the detector and return it as as np.array corrected for the cross.
- grab_image_from_detector_debug(fileName)
- apply_image_corrections()
- apply_flatfield_correction(image)
- correct_deadpixels(img)
correct the dead pixels of the collected image, by using a known dead_pixels_map. the dead pixels are replaced by the mean of the neighboring pixels. return the resulting image as 1D vector
- correctCross(raw, factor=3)
correct the cross of the collected image from (512,512) to (516,516). the intensity from the neighboring pixels is divided by the factor value and assigned to the “cross” pixels. return the corrected image as a np.array of shape (516,516) the intensity of the reflections inside the cross are splitted in 3 consequent pixels
- 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
- pyfast_adt.main.adaptor.camera.adaptor_timepix1.tpxDetectorID = 0