class FRMJob(PyTomClass): # i need to rename the class, but for now it works
def __init__(self,
pl=None,
ref=None,
mask=None,
peak_offset=0,
sample_info=None,
bw_range=None,
freq=None,
dest='.',
max_iter=10,
r_score=False,
weighting=False,
bfactor=None,
symmetries=None,
adaptive_res=0.1,
fsc_criterion=0.5,
constraint=None,
binning=1):
"""
initiate FRM job
@param pl: particle list
@type ps: L{pytom.basic.structures.ParticleList}
@param ref: reference density
@type ref: L{pytom.basic.structures.Reference}
@param mask: mask
@type ref: L{pytom.basic.structures.Mask}
@param peak_offset: peak offset in voxel
@type peak_offset: C{int}
@param sample_info: ?? (Default: None)
@type sample_info: ??
@param bw_range: bandwidth range in pixel (2-dim vector)
@type bw_range: C{list}
@param freq: frequency (default: None)
@type: C{int}
@param dest: distination directory (default: '.')
@type: C{str}
@param max_iter: maximum number of iterations
@type max_iter: C{int}
@param r_score: use r_score (??) (default: False)
@type r_score: C{bool}
@param weighting: weighting (default: False)
@type weighting: C{bool}
@param bfactor: B-factor (default: None)
@type bfactor: C{float}?
@param symmetries: symmetry (default: None)
@type L{pytom.basic.structures.Symmetries}
@param adaptive_res: adaptive resolution - add to resolution for filtering
@type adaptive_res: C{float}
@param fsc_criterion: FSC criterion (default: 0.5)
@type fsc_criterion: C{float}
@param constraint: Constraint on orientations (deafult: None)
@type constraint: ??
@param binning: Perform binning (downscale) of subvolumes by factor. Default=1.
@type binning C{float}
"""
self.particleList = pl
self.reference = ref
self.mask = mask
self.peak_offset = peak_offset
self.sampleInformation = sample_info
self.bw_range = bw_range
self.freq = freq
self.destination = dest
self.max_iter = max_iter
self.r_score = r_score
self.weighting = weighting
self.bfactor = bfactor
self.symmetries = symmetries
self.adaptive_res = adaptive_res
self.fsc_criterion = fsc_criterion
self.constraint = constraint
self.binning = binning
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 toXML(self):
"""
copy to xml structure
@return: xml object for job
@rtype L{lxml.etree.Element}
"""
from lxml import etree
jobElement = etree.Element("FRMJob")
jobElement.append(self.particleList.toXML())
jobElement.append(self.reference.toXML())
jobElement.append(self.mask.toXML())
jobElement.append(self.sampleInformation.toXML())
if self.symmetries is not None:
jobElement.append(self.symmetries.toXML())
jobElement.set("PeakOffset", str(self.peak_offset))
jobElement.set("BandwidthRange", str(self.bw_range))
jobElement.set("Frequency", str(self.freq))
jobElement.set("Destination", self.destination)
jobElement.set("MaxIterations", str(self.max_iter))
jobElement.set("RScore", str(self.r_score))
jobElement.set("WeightedAverage", str(self.weighting))
jobElement.set("BFactor", str(self.bfactor))
jobElement.set("binning", str(self.binning))
if self.adaptive_res is False:
jobElement.set("AdaptiveResolution", '+1')
else:
jobElement.set("AdaptiveResolution", str(self.adaptive_res))
jobElement.set("FSC", str(self.fsc_criterion))
if self.constraint:
jobElement.append(self.constraint.toXML())
return jobElement
def check(self):
from pytom.tools.files import checkDirExists
self.particleList.check()
self.reference.check()
self.mask.check()
if not checkDirExists(self.destination):
raise RuntimeError('Destination path not found! ' +
self.destination)
class CTFCorrectionJob(PyTomClass):
"""For the worker to actually run.
"""
def __init__(self,
pl=None,
ctf_conv_pl=None,
ref=None,
mask=None,
peak_offset=0,
sample_info=None,
bw_range=None,
freq=None,
dest='.',
max_iter=10,
r_score=False,
weighting=False,
bfactor=None,
sum_ctf_sqr=None):
self.particleList = pl
self.ctf_conv_pl = ctf_conv_pl
if ref is None:
self.reference = []
else:
self.reference = ref
self.mask = mask
self.peak_offset = peak_offset
self.sampleInformation = sample_info
self.bw_range = bw_range
self.freq = freq
self.destination = dest
self.max_iter = max_iter
self.r_score = r_score
self.weighting = weighting
self.bfactor = bfactor
self.sum_ctf_sqr = sum_ctf_sqr
def fromXML(self, xmlObj):
from lxml.etree import _Element
if xmlObj.__class__ != _Element:
raise Exception('You must provide a valid XML object.')
if xmlObj.tag == "CTFCorrectionJob":
jobDescription = xmlObj
else:
jobDescription = xmlObj.xpath('CTFCorrectionJob')
if len(jobDescription) == 0:
raise Exception("This XML is not a CTFCorrectionJob.")
jobDescription = jobDescription[0]
from pytom.basic.structures import ParticleList, Reference, Mask, SampleInformation
particleList_element = jobDescription.xpath('ParticleList')[0]
pl = ParticleList('.')
pl.fromXML(particleList_element)
self.particleList = pl
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()
self.ctf_conv_pl = jobDescription.get('CTFConvolutedParticleList')
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.sum_ctf_sqr = jobDescription.get('CTFSquared')
def toXML(self):
from lxml import etree
jobElement = etree.Element("CTFCorrectionJob")
jobElement.append(self.particleList.toXML())
for ref in self.reference:
jobElement.append(ref.toXML())
jobElement.append(self.mask.toXML())
jobElement.append(self.sampleInformation.toXML())
jobElement.set("CTFConvolutedParticleList", self.ctf_conv_pl)
jobElement.set("PeakOffset", str(self.peak_offset))
jobElement.set("BandwidthRange", str(self.bw_range))
jobElement.set("Frequency", str(self.freq))
jobElement.set("Destination", self.destination)
jobElement.set("MaxIterations", str(self.max_iter))
jobElement.set("RScore", str(self.r_score))
jobElement.set("WeightedAverage", str(self.weighting))
jobElement.set("BFactor", str(self.bfactor))
jobElement.set("CTFSquared", str(self.sum_ctf_sqr))
return jobElement
class FRMJob(PyTomClass): # i need to rename the class, but for now it works
def __init__(self,
pl=None,
ref=None,
mask=None,
peak_offset=0,
sample_info=None,
bw_range=None,
freq=None,
dest='.',
max_iter=10,
r_score=False,
weighting=False,
bfactor=None,
symmetries=None,
adaptive_res=0.1,
fsc_criterion=0.5,
constraint=None):
self.particleList = pl
self.reference = ref
self.mask = mask
self.peak_offset = peak_offset
self.sampleInformation = sample_info
self.bw_range = bw_range
self.freq = freq
self.destination = dest
self.max_iter = max_iter
self.r_score = r_score
self.weighting = weighting
self.bfactor = bfactor
self.symmetries = symmetries
self.adaptive_res = adaptive_res
self.fsc_criterion = fsc_criterion
self.constraint = constraint
def fromXML(self, xmlObj):
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')
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 toXML(self):
from lxml import etree
jobElement = etree.Element("FRMJob")
jobElement.append(self.particleList.toXML())
jobElement.append(self.reference.toXML())
jobElement.append(self.mask.toXML())
jobElement.append(self.sampleInformation.toXML())
if self.symmetries is not None:
jobElement.append(self.symmetries.toXML())
jobElement.set("PeakOffset", str(self.peak_offset))
jobElement.set("BandwidthRange", str(self.bw_range))
jobElement.set("Frequency", str(self.freq))
jobElement.set("Destination", self.destination)
jobElement.set("MaxIterations", str(self.max_iter))
jobElement.set("RScore", str(self.r_score))
jobElement.set("WeightedAverage", str(self.weighting))
jobElement.set("BFactor", str(self.bfactor))
if self.adaptive_res is False:
jobElement.set("AdaptiveResolution", '+1')
else:
jobElement.set("AdaptiveResolution", str(self.adaptive_res))
jobElement.set("FSC", str(self.fsc_criterion))
if self.constraint:
jobElement.append(self.constraint.toXML())
return jobElement
def check(self):
from pytom.tools.files import checkDirExists
self.particleList.check()
self.reference.check()
self.mask.check()
if not checkDirExists(self.destination):
raise RuntimeError('Destination path not found! ' +
self.destination)
class MultiDefocusJob(FRMJob):
"""For the entry of the whole procedure.
"""
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
def check(self):
for p in self.particleList.pairs:
p.check()