def filter_frames(self, data):
print '############ ONE FILTER FRAME ########################'
bc = [0, 0]
distance, bc[0], bc[1], yaw, roll, px, px = self.ai_params
t1 = time.time()
data = data[0].astype(np.float)
data -= np.min(data)
mData = self.params[1]
# print self.params[2]
# foo = self.params[2]
ai = AzimuthalIntegrator()
ai.setFit2D(distance, bc[0], bc[1], yaw, roll, px, px, None)
ai.set_wavelength(self.wl)
foo = units.to_unit(units.TTH)
twotheta = ai.__getattribute__(foo.center)(
self.sh) # get the detector array of Q # preallocate
twotheta *= 180.0 / np.pi # preallocate
twotheta_flat = twotheta.ravel() #pre allocate
logging.debug(ai)
# twotheta_flat = self.params[3]
logging.debug("Running azimuthal integration")
mask = np.zeros_like(data, np.int)
fit = ai.integrate1d(data=data,
npt=self.npts,
unit='q_A^-1',
correctSolidAngle=False)
print "here"
mData.set_meta_data('Q', fit[0])
newplot = self.calcfrom1d(fit[0], fit[1], twotheta_flat)
rat = data / newplot
thing = data.copy()
thing[np.abs(rat - 1.0) > self.parameters['thresh'] * 1e-2] = 0.0
mask[thing == 0] = 1
# mask[data < minval] = 0
#mask[data > maxval] = 0
# final = ai.integrate1d(data=data, npt=self.npts,unit='q_A^-1', correctSolidAngle=False)# works...
del ai
ai = AzimuthalIntegrator()
ai.setFit2D(distance, bc[0], bc[1], yaw, roll, px, px, None)
ai.set_wavelength(self.wl)
print "DATA.shape is %s " % str(data.shape)
final = ai.integrate1d(data,
self.npts,
mask=mask,
unit='q_A^-1',
correctSolidAngle=False,
method='numpy') # doesn't work
# final = ai.integrate1d(data=data, npt=self.npts,mask=mask,unit='q_A^-1', correctSolidAngle=False)# doesn't work
t2 = time.time()
del ai
# print final[1].shape
print "PyFAI iteration with correction took:" + str(
(t2 - t1) * 1e3) + "ms"
print '##################END##################'
return final[1]
def test_AzimuthalIntegrator_pickle():
import dill
import numpy as np
from pyFAI import AzimuthalIntegrator
det = pyFAI.detectors.detector_factory('pilatus2m')
ai = AzimuthalIntegrator(detector=det)
ai.set_wavelength(.1)
spectra = ai.integrate1d(np.ones(det.shape), 1000) # force lut generation
dump = dumps(ai)
newai = loads(*dump)
assert np.array_equal(newai.integrate1d(np.ones(det.shape), 1000), spectra)
assert newai.detector.shape == (1679, 1475)
def test_FourierCalibrationWorkflow():
workflow = FourierCalibrationWorkflow()
data = fabio.open('/home/rp/data/YL1031/AGB_5S_USE_2_2m.edf').data
ai = AzimuthalIntegrator()
ai.set_wavelength(124e-12)
ai.detector = detectors.Pilatus2M()
c = calibrant.ALL_CALIBRANTS('AgBh')
print(
workflow.execute(None,
data=data,
ai=ai,
calibrant=c,
callback_slot=print))
def pre_process(self):
in_dataset, out_datasets = self.get_datasets()
mData = self.get_in_meta_data()[0]
in_d1 = in_dataset[0]
ai = AzimuthalIntegrator() # get me an integrator object
# prep the goemtry
px_m = mData.get_meta_data('x_pixel_size')
bc_m = [
mData.get_meta_data("beam_center_x"),
mData.get_meta_data("beam_center_y")
] # in metres
bc = bc_m / px_m # convert to pixels
px = px_m * 1e6 # convert to microns
distance = mData.get_meta_data('distance') * 1e3 # convert to mm
wl = mData.get_meta_data('incident_wavelength')[...] # in m
self.wl = wl
yaw = -mData.get_meta_data("yaw")
roll = mData.get_meta_data("roll")
self.ai_params = distance, bc[0], bc[1], yaw, roll, px, px
ai.setFit2D(distance, bc[0], bc[1], yaw, roll, px, px, None)
ai.set_wavelength(wl)
logging.debug(ai)
self.sh = in_d1.get_shape()[-2:]
self.npts = self.get_parameters('num_bins')
foo = units.to_unit(units.TTH) # preallocate
pretenddata = np.zeros(self.sh)
pretendfit = ai.integrate1d(data=pretenddata,
npt=self.npts,
unit='q_A^-1',
correctSolidAngle=False)
self.add_axes_to_meta_data(pretendfit[0], mData)
twotheta = ai.__getattribute__(foo.center)(
self.sh) # get the detector array of Q # preallocate
twotheta *= 180.0 / np.pi # preallocate
twotheta_flat = twotheta.ravel() #pre allocate
self.params = [self.npts, mData, twotheta_flat]
def pre_process(self):
"""
This method is called after the plugin has been created by the
pipeline framework as a pre-processing step
:param parameters: A dictionary of the parameters for this plugin, or
None if no customisation is required
:type parameters: dict
"""
in_dataset, out_datasets = self.get_datasets()
mData = self.get_in_meta_data()[0]
in_d1 = in_dataset[0]
ai = AzimuthalIntegrator() # get me an integrator object
### prep the goemtry
bc = [mData.get_meta_data("beam_center_x")[...],
mData.get_meta_data("beam_center_y")[...]]
distance = mData.get_meta_data('distance')[...]
wl = mData.get_meta_data('incident_wavelength')[...]
px = mData.get_meta_data('x_pixel_size')[...]
orien = mData.get_meta_data(
'detector_orientation')[...].reshape((3, 3))
#Transform
yaw = math.degrees(-math.atan2(orien[2, 0], orien[2, 2]))
roll = math.degrees(-math.atan2(orien[0, 1], orien[1, 1]))
ai.setFit2D(distance, bc[0], bc[1], -yaw, roll, px, px, None)
ai.set_wavelength(wl)
self.sh = in_d1.get_shape()[-2:]
self.npts = self.get_parameters('num_bins')
foo = units.to_unit(units.TTH) # preallocate
pretenddata = np.zeros(self.sh)
pretendfit = ai.xrpd(data=pretenddata, npt=self.npts)
twotheta=ai.__getattribute__(foo.center)(ai.detector.max_shape) # get the detector array of Q # preallocate
twotheta *= 180.0/np.pi # preallocate
twotheta_flat = twotheta.ravel() #pre allocate
self.params = [self.npts, mData, ai, twotheta_flat]
