def fromXML(self, xmlObj):
"""
read from xml file
@param xmlObj: xml object
@type xmlObj: L{lxml.etree.Element}
"""
from lxml.etree import _Element
if xmlObj.__class__ != _Element:
raise Exception('You must provide a valid XML object.')
if xmlObj.tag == "FRMJob":
jobDescription = xmlObj
else:
jobDescription = xmlObj.xpath('FRMJob')
if len(jobDescription) == 0:
raise Exception("This XML is not a FRMJob.")
jobDescription = jobDescription[0]
from pytom.basic.structures import ParticleList, Reference, Mask, SampleInformation, MultiSymmetries
pl = ParticleList('.')
particleList_element = jobDescription.xpath('ParticleList')
if len(particleList_element) > 0:
pl.fromXML(particleList_element[0])
else:
list_elements = jobDescription.xpath('ParticleListLocation')
for e in list_elements:
sub_pl = ParticleList()
sub_pl.fromXMLFile(e.get('Path'))
pl += sub_pl
self.particleList = pl
r = jobDescription.xpath('Reference')[0]
self.reference = Reference('')
self.reference.fromXML(r)
m = jobDescription.xpath('Mask')[0]
self.mask = Mask('')
self.mask.fromXML(m)
try:
si = jobDescription.xpath('SampleInformation')[0]
self.sampleInformation = SampleInformation()
self.sampleInformation.fromXML(si)
except:
self.sampleInformation = SampleInformation()
try:
syms = jobDescription.xpath('MultiSymmetries')[0]
self.symmetries = MultiSymmetries()
self.symmetries.fromXML(syms)
except:
self.symmetries = MultiSymmetries()
self.peak_offset = int(jobDescription.get('PeakOffset'))
self.bw_range = [
int(i)
for i in jobDescription.get('BandwidthRange')[1:-1].split(',')
]
self.freq = int(jobDescription.get('Frequency'))
self.destination = jobDescription.get('Destination')
self.max_iter = int(jobDescription.get('MaxIterations'))
self.r_score = jobDescription.get('RScore') == 'True'
self.weighting = jobDescription.get('WeightedAverage') == 'True'
self.bfactor = jobDescription.get('BFactor')
self.binning = int(jobDescription.get('binning'))
if jobDescription.get('AdaptiveResolution'):
adaptive_resolution = jobDescription.get('AdaptiveResolution')
if adaptive_resolution == '+1':
self.adaptive_res = False # always increase by 1
else:
self.adaptive_res = float(adaptive_resolution)
else:
self.adaptive_res = 0.0 # default, if not specified
if jobDescription.get('FSC'):
self.fsc_criterion = float(jobDescription.get('FSC'))
else:
self.fsc_criterion = 0.5 # default value
# for the constraint
try:
from sh_alignment.constrained_frm import AngularConstraint
con = jobDescription.xpath('AngularConstraint')
if len(con) != 0:
ac = AngularConstraint()
c = ac.fromXML(con[0])
self.constraint = c
else:
self.constraint = None
except:
self.constraint = None
def fromXML(self, xmlObj): # only rewrite this function
from lxml.etree import _Element
if xmlObj.__class__ != _Element:
raise Exception('You must provide a valid XML object.')
if xmlObj.tag == "FRMJob": # the name is not changed here!
jobDescription = xmlObj
else:
jobDescription = xmlObj.xpath('FRMJob')
if len(jobDescription) == 0:
raise Exception("This XML is not a FRMJob.")
jobDescription = jobDescription[0]
from pytom.basic.structures import Reference, Mask, SampleInformation, MultiSymmetries
particleList_element = jobDescription.xpath('ParticleListSet')[0]
pl = ParticleListSet()
pl.fromXML(particleList_element)
self.particleList = pl # here i still use the original name!
self.reference = []
r = jobDescription.xpath('Reference')
for ref_obj in r:
ref = Reference('')
ref.fromXML(ref_obj)
self.reference.append(ref)
m = jobDescription.xpath('Mask')[0]
self.mask = Mask('')
self.mask.fromXML(m)
try:
si = jobDescription.xpath('SampleInformation')[0]
self.sampleInformation = SampleInformation()
self.sampleInformation.fromXML(si)
except:
self.sampleInformation = SampleInformation()
try:
syms = jobDescription.xpath('MultiSymmetries')[0]
self.symmetries = MultiSymmetries()
self.symmetries.fromXML(syms)
except:
self.symmetries = MultiSymmetries()
self.peak_offset = int(jobDescription.get('PeakOffset'))
self.bw_range = [
int(i)
for i in jobDescription.get('BandwidthRange')[1:-1].split(',')
]
self.freq = int(jobDescription.get('Frequency'))
self.destination = jobDescription.get('Destination')
self.max_iter = int(jobDescription.get('MaxIterations'))
self.r_score = jobDescription.get('RScore') == 'True'
self.weighting = jobDescription.get('WeightedAverage') == 'True'
self.bfactor = jobDescription.get('BFactor')
if jobDescription.get('AdaptiveResolution'):
adaptive_resolution = jobDescription.get('AdaptiveResolution')
if adaptive_resolution == '+1':
self.adaptive_res = False # always increase by 1
else:
self.adaptive_res = float(adaptive_resolution)
else:
self.adaptive_res = 0.0 # default, if not specified
if jobDescription.get('FSC'):
self.fsc_criterion = float(jobDescription.get('FSC'))
else:
self.fsc_criterion = 0.5 # default value