def _defineParamDict(self):
""" Define all parameters to run relion_postprocess"""
volume = self.protRefine.get().outputVolume
# It seems that in Relion3 now the input should be the map
# filename and not the prefix as before
if convert.isVersion3():
inputFn = self._getFileName('half1')
else:
inputFn = self._getInputPath("relion")
cps = self.calibratedPixelSize.get()
angpix = cps if cps > 0 else volume.getSamplingRate()
self.paramDict = {
'--i': inputFn,
'--o': self._getExtraPath('relion'),
'--angpix': angpix,
'--adhoc_bfac': self.bfactor.get(),
'--locres': '',
# Expert options
'--locres_sampling': self.locResSamp.get(),
'--locres_maskrad': self.locResMaskRad.get(),
'--locres_edgwidth': self.locResEdgeWidth.get(),
'--locres_randomize_at': self.locResRand.get(),
'--locres_minres': self.locResMin.get()
}
mtfFile = self.mtf.get()
if mtfFile:
self.paramDict['--mtf'] = mtfFile
def _defineParamDict(self):
""" Define all parameters to run relion_postprocess"""
volume = self.protRefine.get().outputVolume
cps = self.calibratedPixelSize.get()
angpix = cps if cps > 0 else volume.getSamplingRate()
# It seems that in Relion3 now the input should be the map
# filename and not the prefix as before
if isVersion3():
inputFn = self._getFileName('half1')
else:
inputFn = self._getInputPath("relion")
self.paramDict = {
'--i': inputFn,
'--o': self._getExtraPath('postprocess'),
'--angpix': angpix,
# Expert params
'--filter_edge_width': self.filterEdgeWidth.get(),
'--randomize_at_fsc': self.randomizeAtFsc.get(),
'--mask': self._getFileName('mask')
}
mtfFile = self.mtf.get()
if mtfFile:
self.paramDict['--mtf'] = mtfFile
if self.doAutoBfactor:
self.paramDict['--auto_bfac'] = ''
self.paramDict['--autob_lowres'] = self.bfactorLowRes.get()
self.paramDict['--autob_highres'] = self.bfactorHighRes.get()
else:
self.paramDict['--adhoc_bfac'] = self.bfactor.get()
if self.skipFscWeighting:
self.paramDict['--skip_fsc_weighting'] = ''
self.paramDict['--low_pass'] = self.lowRes.get()
def isDisabled(cls):
return isVersion2() or isVersion3()
def _defineParams(self, form):
form.addSection(label='Input')
form.addParam('inputMicrographs',
params.PointerParam,
pointerClass='SetOfMicrographs',
label='Input micrographs',
important=True,
help='Select the input micrographs. '
'If using the *Optimize* mode, just a subset of '
'micrographs are used to compute the FOM maps. '
'If in *Compute* mode, all micrographs will be '
'auto-picked.')
form.addParam('ctfRelations',
params.RelationParam,
relationName=RELATION_CTF,
attributeName='getInputMicrographs',
label='CTF estimation',
help='Choose some CTF estimation related to the '
'input micrographs.')
form.addParam('runType',
params.EnumParam,
default=RUN_OPTIMIZE,
choices=['Optimize params', 'Pick all micrographs'],
display=params.EnumParam.DISPLAY_LIST,
label='Run type: ',
help='Usually, first you should use the *Optimize* mode '
'to compute the FOM maps for a few micrographs and '
'use them to tune the picking parameters using the '
'wizard. After that you can run the job in *Compute*'
' mode and auto-pick all the micrographs. ')
group = form.addGroup('Micrographs for optimization',
condition='runType==%d' % RUN_OPTIMIZE)
group.addParam('micrographsSelection',
params.EnumParam,
default=MICS_AUTO,
choices=['automatic selection', 'input subset'],
display=params.EnumParam.DISPLAY_HLIST,
label='Choose micrographs by',
help='If you choose "automatic selection", you only '
'need to provide the number of microgrphs to use '
'and that number will be selected to cover the '
'defocus range. ')
group.addParam('micrographsNumber',
params.IntParam,
default='10',
condition='micrographsSelection==%d' % MICS_AUTO,
label='Micrographs for optimization:',
help='Select the number of micrographs that you want'
'to be used for the parameters optimization. ')
group.addParam('micrographsSubset',
params.PointerParam,
condition='micrographsSelection==%d' % MICS_SUBSET,
pointerClass='SetOfMicrographs',
label='Subset of micrographs',
help='Choose as input a subset of micrographs that '
'you have previously selected. '
'(Probably covering the defocus range).')
# From Relion 2.+, it can be picked with gaussian blobs, so we
# need to add these parameters
refCondition = 'referencesType==%s' % REF_AVERAGES
group = form.addGroup('References')
group.addParam('referencesType',
params.EnumParam,
choices=['References', 'Gaussian blobs'],
default=REF_AVERAGES,
display=params.EnumParam.DISPLAY_HLIST,
label='References type',
help='You may select "Gaussian blobs" to be used as '
'references. The preferred way to autopick is '
'by providing 2D references images that were '
'obtained by 2D classification. \n'
'The Gaussian blob references may be useful to '
'kickstart a new data set.')
group.addParam('gaussianPeak',
params.FloatParam,
default=0.1,
condition='referencesType==%s' % REF_BLOBS,
label='Gaussian peak value',
help='The peak value of the Gaussian blob. '
'Weaker data will need lower values.')
pointerClassStr = 'SetOfAverages'
# In Relion 3 it is also possible to pass a volume as reference for
# autopicking
if isVersion3():
pointerClassStr += ",Volume"
group.addParam(
'inputReferences',
params.PointerParam,
pointerClass='SetOfAverages',
condition=refCondition,
label='Input references',
important=True,
help='Input references (SetOfAverages) for auto-pick. \n\n'
'Note that the absolute greyscale needs to be correct, \n'
'so only use images with proper normalization. '
'From Relion 3.0 it is also possible to provide a '
'3D volume which projections will be used as '
'references.')
group.addParam(
'particleDiameter',
params.IntParam,
default=-1,
label='Mask diameter (A)',
help='Diameter of the circular mask that will be applied '
'around the templates in Angstroms. When set to a '
'negative value, this value is estimated '
'automatically from the templates themselves.')
form.addSection('References')
form.addParam('lowpassFilterRefs',
params.IntParam,
default=20,
condition=refCondition,
label='Lowpass filter references (A)',
help='Lowpass filter that will be applied to the '
'references before template matching. \n'
'Do NOT use very high-resolution templates to '
'search your micrographs. \n'
'The signal will be too weak at high resolution '
'anyway, and you may find Einstein from noise...')
form.addParam('highpassFilterMics',
params.IntParam,
default=-1,
label='Highpass filter (A)',
help='Highpass filter that will be applied to the '
'micrographs. This may be useful to get rid of '
'background ramps due to uneven ice distributions. '
'Give a negative value to skip the highpass '
'filter. Useful values are often in the range '
'of 200-400 Angstroms.')
form.addParam(
'angularSampling',
params.IntParam,
default=5,
label='Angular sampling (deg)',
help='Angular sampling in degrees for exhaustive searches '
'of the in-plane rotations for all references.')
form.addParam('refsHaveInvertedContrast',
params.BooleanParam,
default=True,
label='References have inverted contrast?',
help='Set to Yes to indicate that the reference have '
'inverted contrast with respect to the particles '
'in the micrographs.')
form.addParam('refsCtfCorrected',
params.BooleanParam,
default=True,
condition=refCondition,
label='Are References CTF corrected?',
help='Set to Yes if the references were created with '
'CTF-correction inside RELION.\n'
'If set to Yes, the input micrographs should contain '
'the CTF information.')
form.addParam(
'ignoreCTFUntilFirstPeak',
params.BooleanParam,
condition=refCondition,
default=False,
expertLevel=params.LEVEL_ADVANCED,
label='Ignore CTFs until first peak?',
help='Set this to Yes, only if this option was also used '
'to generate the references.')
form.addSection('Autopicking')
group = form.addGroup('Autopick')
group.addParam('pickingThreshold',
params.FloatParam,
default=0.25,
label='Picking threshold:',
help='Use lower thresholds to pick more particles '
'(and more junk probably)')
group.addParam('interParticleDistance',
params.IntParam,
default=-1,
label='Minimum inter-particle distance (A):',
help='Particles closer together than this distance \n'
'will be consider to be a single cluster. \n'
'From each cluster, only one particle will be '
'picked.')
group.addParam(
'maxStddevNoise',
params.FloatParam,
default=1.1,
label='Maximum stddev noise:',
help='This is useful to prevent picking in carbon areas, '
'or areas with big contamination features. Peaks in '
'areas where the background standard deviation in '
'the normalized micrographs is higher than this '
'value will be ignored. Useful values are probably '
'in the range 1.0 to 1.2. Set to -1 to switch off '
'the feature to eliminate peaks due to high '
'background standard deviations.')
group = form.addGroup('Computing')
group.addParam('shrinkFactor',
params.FloatParam,
default=0,
validators=[
params.Range(0, 1, "value should be "
"between 0 and 1. ")
],
label='Shrink factor',
help='This is useful to speed up the calculations, '
'and to make them less memory-intensive. The '
'micrographs will be downscaled (shrunk) to '
'calculate the cross-correlations, and peak '
'searching will be done in the downscaled FOM '
'maps. When set to 0, the micrographs will de '
'downscaled to the lowpass filter of the '
'references, a value between 0 and 1 will '
'downscale the micrographs by that factor. '
'Note that the results will not be exactly '
'the same when you shrink micrographs!')
group.addParam('doGpu',
params.BooleanParam,
default=True,
label='Use GPU acceleration?',
help='If set to Yes, the job will try to use GPU '
'acceleration.')
group.addParam('gpusToUse',
params.StringParam,
default='',
label='Which GPUs to use:',
condition='doGpu',
help='This argument is not necessary. If left empty, '
'the job itself will try to allocate available GPU '
'resources. You can override the default '
'allocation by providing a list of which GPUs '
'(0,1,2,3, etc) to use. MPI-processes are '
'separated by ":", threads by ",". '
'For example: "0,0:1,1:0,0:1,1"')
form.addParam(
'extraParams',
params.StringParam,
default='',
label='Additional arguments:',
help='In this box command-line arguments may be provided '
'that are not generated by the GUI. This may be '
'useful for testing developmental options and/or '
'expert use of the program. \n'
'The command "relion_autopick" will print a list '
'of possible options.')
form.addSection('Helix')
form.addParam('fomLabel',
params.LabelParam,
important=True,
label='Helix processing is not implemented still.')
self._defineStreamingParams(form)
form.addParallelSection(threads=0, mpi=4)
def isDisabled(cls):
return not isVersion3()
# * You should have received a copy of the GNU General Public License
# * along with this program; if not, write to the Free Software
# * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA
# * 02111-1307 USA
# *
# * All comments concerning this program package may be sent to the
# * e-mail address '*****@*****.**'
# *
# **************************************************************************
from pyworkflow import VERSION_1_1
from pyworkflow.em import ProtCreateMask3D, VolumeMask
from convert import convertBinaryVol, isVersion1, isVersion3
import pyworkflow.protocol.params as params
NOT_VERSION1 = not isVersion1()
IS_V3 = isVersion3()
AND = 0
OR = 1
AND_NOT = 2
OR_NOT = 3
class ProtRelionCreateMask3D(ProtCreateMask3D):
""" Create a 3D mask.
The mask is created from a 3d volume or by comparing two input volumes.
"""
_label = 'create 3d mask'
_lastUpdateVersion = VERSION_1_1
# --------------------------- DEFINE param functions ------------------------
def isDisabled(cls):
return not convert.isVersion3()