def sphereModel(evt, type, key_centerx, key_centery, key_diameter, key_intensity,
shape, wavelength=1., pixelsize=110, distance=1000, adu_per_photon=1,
quantum_efficiency=1, material='virus', poisson=False):
"""Return sphere model.
.. note:: For this function, `libspimage <https://github.com/FilipeMaia/libspimage>`_ needs to be installed.
Args:
:evt: The event variable
:type(str): The event type, e.g. analysis
:key_centerx(str): The event key of the estimated off center shift in x
:key_centery(str): The event key of the estimated off center shift in y
:key_diameter(str): The event key of the estimated diameter
:key_intensity(str): The event key of the estimated intensity
:shape(tuple): The shape of the fit
Kwargs:
:wavelength(float): Photon wavelength [nm] (default = 1)
:pixelsize(int): Side length of a pixel [um] (default=110)
:distance(int): Distance from interaction to detector [mm] (default = 1000)
:adu_per_photon(int): ADUs per photon (default = 1)
:quantum_efficiency(float): Quantum efficiency of the detector (default = 1)
:material(str): Material of particle, e.g. virus, protein, water, ... (default = virus)
:poisson(bool): If True, apply poisson sampling (default = False)
:Authors:
Benedikt J. Daurer ([email protected]),
Max Hantke,
Filipe Maia
"""
if not spimage_installed:
print "For sizing.sphereModel, libspimage (https://github.com/FilipeMaia/libspimage) needs to be installed"
return
centerx = evt[type][key_centerx].data
centery = evt[type][key_centery].data
diameter = evt[type][key_diameter].data * 1e-9
intensity = evt[type][key_intensity].data * 1e-3 / 1e-12
wavelength *= 1e-9
distance *= 1e-3
pixelsize *= 1e-6
size = spimage.sphere_model_convert_diameter_to_size(diameter, wavelength,
pixelsize, distance)
scaling = spimage.sphere_model_convert_intensity_to_scaling(intensity, diameter,
wavelength, pixelsize,
distance, quantum_efficiency,
adu_per_photon, material)
fit = spimage.I_sphere_diffraction(scaling,
spimage.rgrid(shape, (centerx, centery)),
size)
if poisson:
fit = np.random.poisson(fit)
add_record(evt["analysis"], "analysis", "fit", fit, unit='ADU')
def find_center_pixelwise_fast(img, msk, x0=0, y0=0, dmax=5, rmax=None):
"""
Find center of diffraction pattern using a pixelwise comparison of centry-symmetric pixels.
This is a faster C implementation.
usage:
======
x,y = find_center_pixelwise_fast(img, msk, x0, y0, dmax=5, rmax=None)
"""
if rmax is not None: msk &= (spimage.rgrid(msk.shape, (x0,y0)) < rmax)
I = spimage.sp_image_alloc(int(np.ceil(img.shape[1])), int(np.ceil(img.shape[0])), 1)
I.image[:] = img
I.mask[:] = msk
I.detector.image_center[:] = np.array([center_to_pos(x0,img.shape[1]), center_to_pos(y0,img.shape[0]), 0 ])
success = spimage.sp_find_center_refine(I, dmax, 0, None)
x = pos_to_center(I.detector.image_center[0],img.shape[1])
y = pos_to_center(I.detector.image_center[1],img.shape[0])
spimage.sp_image_free(I)
return (x,y,success)
def find_center_pixelwise_fast(img, msk, x0=0, y0=0, dmax=5, rmax=None):
"""
Find center of diffraction pattern using a pixelwise comparison of centry-symmetric pixels.
This is a faster C implementation.
usage:
======
x,y = find_center_pixelwise_fast(img, msk, x0, y0, dmax=5, rmax=None)
"""
if rmax is not None: msk &= (spimage.rgrid(msk.shape, (x0,y0)) < rmax)
I = spimage.sp_image_alloc(int(np.ceil(img.shape[1])), int(np.ceil(img.shape[0])), 1)
I.image[:] = img
I.mask[:] = msk
I.detector.image_center[:] = np.array([center_to_pos(x0,img.shape[1]), center_to_pos(y0,img.shape[0]), 0 ])
success = spimage.sp_find_center_refine(I, dmax, 0, None)
x = pos_to_center(I.detector.image_center[0],img.shape[1])
y = pos_to_center(I.detector.image_center[1],img.shape[0])
spimage.sp_image_free(I)
return (x,y,success)
