def setPositions(self, in_meta_data):
paramdict = XRFDataset().paramdict
paramdict["FitParams"]["pileup_cutoff_keV"] = \
self.parameters["pileup_cutoff_keV"]
paramdict["FitParams"]["include_pileup"] = \
self.parameters["include_pileup"]
paramdict["FitParams"]["include_escape"] = \
self.parameters["include_escape"]
paramdict["FitParams"]["fitted_energy_range_keV"] = \
self.parameters["fitted_energy_range_keV"]
if self.parameters['mono_energy'] is None:
paramdict["Experiment"]["incident_energy_keV"] = \
in_meta_data.get_meta_data("mono_energy")
else:
paramdict["Experiment"]["incident_energy_keV"] = \
self.parameters['mono_energy']
paramdict["Experiment"]["elements"] = \
self.parameters["elements"]
engy = self.findLines(paramdict)
# make it an index since this is what find peak will also give us
# print 'basefluo meta is:'+str(in_meta_data.get_dictionary().keys())
axis = self.axis = in_meta_data.get_meta_data("energy")
dq = axis[1] - axis[0]
logging.debug("the peak energies are:" + str(engy))
logging.debug("the offset is" + str(axis[0]))
self.idx = np.round((engy - axis[0]) / dq).astype(int)
return self.idx
def findLines(self, paramdict=XRFDataset().paramdict):
"""
Calculates the line energies to fit
"""
# Incident Energy used in the experiment
# Energy range to use for fitting
pileup_cut_off = paramdict["FitParams"]["pileup_cutoff_keV"]
include_pileup = paramdict["FitParams"]["include_pileup"]
include_escape = paramdict["FitParams"]["include_escape"]
fitting_range = paramdict["FitParams"]["fitted_energy_range_keV"]
# x = paramdict["FitParams"]["mca_energies_used"]
energy = paramdict["Experiment"]["incident_energy_keV"]
detectortype = 'Vortex_SDD_Xspress'
fitelements = paramdict["Experiment"]["elements"]
peakpos = []
escape_peaks = []
for _j, el in enumerate(fitelements):
z = xl.SymbolToAtomicNumber(str(el))
for i, shell in enumerate(shells):
if (xl.EdgeEnergy(z, shell) < energy - 0.5):
linepos = 0.0
count = 0.0
for line in transitions[i]:
en = xl.LineEnergy(z, line)
if (en > 0.0):
linepos += en
count += 1.0
if (count == 0.0):
break
linepos = linepos / count
if (linepos > fitting_range[0]
and linepos < fitting_range[1]):
peakpos.append(linepos)
peakpos = np.array(peakpos)
too_low = set(list(peakpos[peakpos > fitting_range[0]]))
too_high = set(list(peakpos[peakpos < fitting_range[1]]))
bar = list(too_low and too_high)
bar = np.unique(bar)
peakpos = list(bar)
peaks = []
peaks.extend(peakpos)
if (include_escape):
for i in range(len(peakpos)):
escape_energy = calc_escape_energy(peakpos[i], detectortype)[0]
if (escape_energy > fitting_range[0]):
if (escape_energy < fitting_range[1]):
escape_peaks.extend([escape_energy])
# print escape_peaks
peaks.extend(escape_peaks)
if (include_pileup): # applies just to the fluorescence lines
pileup_peaks = []
peakpos1 = np.array(peakpos)
peakpos_high = peakpos1[peakpos1 > pileup_cut_off]
peakpos_high = list(peakpos_high)
for i in range(len(peakpos_high)):
foo = [peakpos_high[i] + x for x in peakpos_high[i:]]
foo = np.array(foo)
pileup_peaks.extend(foo)
pileup_peaks = np.unique(sorted(pileup_peaks))
peaks.extend(pileup_peaks)
peakpos = peaks
peakpos = np.array(peakpos)
too_low = set(list(peakpos[peakpos > fitting_range[0]]))
too_high = set(list(peakpos[peakpos < fitting_range[1] - 0.5]))
bar = list(too_low and too_high)
bar = np.unique(bar)
peakpos = list(bar)
peakpos = np.unique(peakpos)
# print peakpos
return peakpos
def prep_xrfd(self, in_meta_data):
self.axis = in_meta_data.get_meta_data("energy")
xrfd = XRFDataset()
# now to overide the experiment
xrfd.paramdict["Experiment"] = {}
xrfd.paramdict["Experiment"]["incident_energy_keV"] = \
self.parameters["mono_energy"]
# print xrfd.paramdict["Experiment"]["incident_energy_keV"]
xrfd.paramdict["Experiment"]["collection_time"] = 1
xrfd.paramdict["Experiment"]['Attenuators'] = \
self.parameters['sample_attenuators']
xrfd.paramdict["Experiment"]['detector_distance'] = \
self.parameters['detector_distance']
xrfd.paramdict["Experiment"]['elements'] = \
self.parameters['elements']
xrfd.paramdict["Experiment"]['incident_angle'] = \
self.parameters['incident_angle']
xrfd.paramdict["Experiment"]['exit_angle'] = \
self.parameters['exit_angle']
xrfd.paramdict["Experiment"]['photon_flux'] = self.parameters['flux']
# overide the fitting parameters
xrfd.paramdict["FitParams"]["background"] = 'strip'
xrfd.paramdict["FitParams"][
"fitted_energy_range_keV"] = self.parameters[
"fitted_energy_range_keV"]
xrfd.paramdict["FitParams"]["include_pileup"] = self.parameters[
"include_pileup"]
xrfd.paramdict["FitParams"]["include_escape"] = self.parameters[
"include_escape"]
datadict = {}
datadict["rows"] = self.get_max_frames()
datadict["cols"] = 1 # we will just treat it as one long row
datadict["average_spectrum"] = np.zeros_like(self.axis)
datadict["Detectors"] = {}
datadict["Detectors"]["type"] = 'Vortex_SDD_Xspress'
xrfd.xrfdata(datadict)
xrfd._createSpectraMatrix()
if xrfd.paramdict['FitParams']['mca_energies_used'] == self.axis:
pass
else:
self.axis = xrfd.paramdict['FitParams']['mca_energies_used']
return xrfd
def pre_process(self, exp):
"""
This method is called after the plugin has been created by the
pipeline framework as a pre-processing step.
"""
in_data_list = self.parameters["in_datasets"]
in_d1 = exp.index["in_data"][in_data_list[0]]
mData = in_d1.meta_data # the metadata
# populate the dictionary from the input parameters
xrfd=XRFDataset()
# now to overide the experiment
xrfd.paramdict["Experiment"]={}
xrfd.paramdict["Experiment"]["incident_energy_keV"] = mData.get_meta_data("mono_energy")/1000.
xrfd.paramdict["Experiment"]["collection_time"] = 1
xrfd.paramdict["Experiment"]['Attenuators'] = self.parameters['sample_attenuators']
xrfd.paramdict["Experiment"]['detector_distance'] = self.parameters['detector_distance']
xrfd.paramdict["Experiment"]['elements'] = self.parameters['fit_elements'].split(',')#['Zn', 'Cu', 'Fe', 'Cr', 'Cl', 'Br', 'Kr']#
xrfd.paramdict["Experiment"]['incident_angle'] = self.parameters['incident_angle']
xrfd.paramdict["Experiment"]['exit_angle'] = self.parameters['exit_angle']
xrfd.paramdict["Experiment"]['photon_flux'] = self.parameters['flux']
# overide the detector
# xrfd.paramdict["Detectors"]={}
# xrfd.paramdict["Detectors"]["type"] = 'Vortex_SDD_Xspress'#str(self.parameters['detector_type'])
# overide the fitting parameters
xrfd.paramdict["FitParams"]["background"] = 'strip'#self.parameters['background']
xrfd.paramdict["FitParams"]["fitted_energy_range_keV"] = [2., 18.]#self.parameters['fit_range']
xrfd.paramdict["FitParams"]["include_pileup"] = 1#self.parameters['include_pileup']
xrfd.paramdict["FitParams"]["include_escape"] = 1#self.parameters['include_escape']
datadict = {}
datadict["rows"] = self.get_max_frames()
datadict["cols"] = 1
datadict["average_spectrum"] = mData.get_meta_data("average")#self.parameters['average_spectrum']
datadict["Detectors"]={}
datadict["Detectors"]["type"] = 'Vortex_SDD_Xspress'
xrfd.xrfdata(datadict)
xrfd._createSpectraMatrix()
#I only know the output shape here!xrfd.matrixdict['Total_Matrix'].shape
params = [xrfd]
return params
def setup(self, experiment):
chunk_size = self.get_max_frames()
#-------------------setup input datasets-------------------------
# get a list of input dataset names required for this plugin
in_data_list = self.parameters["in_datasets"]
# get all input dataset objects
in_d1 = experiment.index["in_data"][in_data_list[0]]
# set all input data patterns
in_d1.set_current_pattern_name("SPECTRUM") # we take in a pattern
# set frame chunk
in_d1.set_nFrames(chunk_size)
#----------------------------------------------------------------
#------------------setup output datasets-------------------------
# get a list of output dataset names created by this plugin
'''
This type of dataset should output a few things I think.
1/ Concentration/sum map (including the channel name)
2/ The average fit
3/ The background/scattering fit
4/ The chisq
5/ The residual (another spectrum I guess...)
if I have these:
for i,j in enumerate(xrfd.matrixdict['Descriptions']):print i,j DICT -descriptions
xrfd.matrixdict['Total_Matrix'] # CHARACTERISTIC CURVES
xrfd.fitdict['parameters'] # WEIGHTS
xrfd.fitdict['chisq'] - NUMBER
xrfd.fitdict['resid'] SPECTRUM
- the rest is easy (ish)
for now, will just hack it slightly...(sorry :-S)
'''
# will force the code to tell me what it will output!DO IT
mData = in_d1.meta_data
datadict = {}
xrfd=XRFDataset()
# first chuck in the fitting parameters
xrfd.paramdict["FitParams"]["background"] = self.parameters['background']
xrfd.paramdict["FitParams"]["fitted_energy_range_keV"] = self.parameters['fit_range']
xrfd.paramdict["FitParams"]["include_pileup"] = self.parameters['include_pileup']
xrfd.paramdict["FitParams"]["include_escape"] = self.parameters['include_escape']
xrfd.paramdict["Experiment"]['elements'] = self.parameters['fit_elements'].split(',')
print xrfd.paramdict["Experiment"]['elements'],type(xrfd.paramdict["Experiment"]['elements'])
datadict["cols"] = 1
datadict["rows"] = 1
datadict["Experiment"]={}
datadict["Experiment"]["incident_energy_keV"] = mData.get_meta_data("mono_energy")/1000.
datadict["Experiment"]["collection_time"] =1. #or indeed anything. This isn't used!
datadict["Detectors"]={}
datadict["Detectors"]["type"] = self.parameters['detector_type']
npts = xrfd.paramdict['Detectors'][datadict["Detectors"]["type"]]['no_of_pixels']
datadict["average_spectrum"] = np.zeros((npts,))#mData.get_meta_data("average")
xrfd.xrfdata(datadict)
#print type(datadict["Experiment"]["incident_energy_keV"])
xrfd._createSpectraMatrix()
metadata=xrfd.matrixdict['Descriptions']
num_els = len(xrfd.matrixdict['Descriptions'])
spectrum_length = len(xrfd.paramdict["FitParams"]["mca_channels_used"][0])
out_data_list = self.parameters["out_datasets"]
out_d1 = experiment.create_data_object("out_data", out_data_list[0])
out_d1.meta_data.copy_dictionary(in_d1.meta_data.get_dictionary(), rawFlag=True)
out_d1.meta_data.set_meta_data('Curve_names',metadata)
characteristic_curves=xrfd.matrixdict["Total_Matrix"]
out_d1.meta_data.set_meta_data('Characteristic_curves',characteristic_curves)
# set pattern for this plugin and the shape
out_d1.set_current_pattern_name("SPECTRUM_STACK")
inshape = in_d1.get_shape()
outshape = inshape[:3]+(num_els, spectrum_length)
out_d1.set_shape(outshape)#
# now to set the output data patterns
out_d1.add_pattern("SPECTRUM_STACK", core_dir = (-2,-1), slice_dir = range(len(outshape)-2))# for some reason I HAVE to have this.... annoying
out_d1.add_pattern("SPECTRUM", core_dir = (-1,), slice_dir = range(len(outshape)-1))
out_d1.add_pattern("CHANNEL", core_dir = (-2,), slice_dir = range(len(outshape)-2).append(-1))
# we haven't changed the motors yet so...
motor_type=mData.get_meta_data("motor_type")
projection = []
projection_slice = []
for item,key in enumerate(motor_type):
if key == 'translation':
projection.append(item)
elif key !='translation':
projection_slice.append(item)
if key == 'rotation':
rotation = item # we will assume one rotation for now to save my headache
projdir = tuple(projection)
projsli = tuple(projection_slice)
if mData.get_meta_data("is_map"):
ndims = range(len(outshape))
ovs = []
for i in ndims:
if i!=projdir[0]:
if i!=projdir[1]:
ovs.append(i)
out_d1.add_pattern("PROJECTION", core_dir = projdir, slice_dir = ovs)# two translation axes
if mData.get_meta_data("is_tomo"):
ndims = range(len(outshape))
ovs = []
for i in ndims:
if i!=rotation:
if i!=projdir[1]:
ovs.append(i)
out_d1.add_pattern("SINOGRAM", core_dir = (rotation,projdir[-1]), slice_dir = ovs)#rotation and fast axis
# set frame chunk
print outshape
out_d1.set_nFrames(chunk_size)
def prep_xrfd(self, in_meta_data):
self.axis = in_meta_data.get_meta_data("energy")
xrfd = XRFDataset()
# now to overide the experiment
xrfd.paramdict["Experiment"] = {}
xrfd.paramdict["Experiment"]["incident_energy_keV"] = self.parameters["mono_energy"]
# print xrfd.paramdict["Experiment"]["incident_energy_keV"]
xrfd.paramdict["Experiment"]["collection_time"] = 1
xrfd.paramdict["Experiment"]["Attenuators"] = self.parameters["sample_attenuators"]
xrfd.paramdict["Experiment"]["detector_distance"] = self.parameters["detector_distance"]
xrfd.paramdict["Experiment"]["elements"] = self.parameters["elements"]
xrfd.paramdict["Experiment"]["incident_angle"] = self.parameters["incident_angle"]
xrfd.paramdict["Experiment"]["exit_angle"] = self.parameters["exit_angle"]
xrfd.paramdict["Experiment"]["photon_flux"] = self.parameters["flux"]
# overide the fitting parameters
xrfd.paramdict["FitParams"]["background"] = "strip"
xrfd.paramdict["FitParams"]["fitted_energy_range_keV"] = self.parameters["fitted_energy_range_keV"]
xrfd.paramdict["FitParams"]["include_pileup"] = self.parameters["include_pileup"]
xrfd.paramdict["FitParams"]["include_escape"] = self.parameters["include_escape"]
datadict = {}
datadict["rows"] = self.get_max_frames()
datadict["cols"] = 1 # we will just treat it as one long row
datadict["average_spectrum"] = np.zeros_like(self.axis)
datadict["Detectors"] = {}
datadict["Detectors"]["type"] = "Vortex_SDD_Xspress"
xrfd.xrfdata(datadict)
xrfd._createSpectraMatrix()
if xrfd.paramdict["FitParams"]["mca_energies_used"] == self.axis:
pass
else:
self.axis = xrfd.paramdict["FitParams"]["mca_energies_used"]
return xrfd