def _insertAllSteps(self):
self.inputStreaming = self.getInputMicrographs().isStreamOpen()
if self.streamingBatchSize > 0 or self.inputStreaming:
# If the input is in streaming, follow the base class policy
# about inserting new steps and discovery new input/output
self.createOutputStep = self._doNothing
ProtParticlePickingAuto._insertAllSteps(self)
else:
# If not in streaming, then we will just insert a single step to
# pick all micrographs at once since it is much faster
self.micDict = {}
self.micDict, _ = self._loadInputList()
self._insertFunctionStep('convertInputStep',
self.getInputMicrographs().strId(),
self.getInputReferences().strId())
nameList = [mic.getMicName() for mic in self.getMicrographList()]
self._insertFunctionStep('pickMicrographListStep', nameList,
*self._getPickArgs())
self._insertFunctionStep('createOutputStep')
# Disable streaming functions:
self._insertFinalSteps = self._doNothing
self._stepsCheck = self._doNothing
def _defineParams(self, form):
ProtParticlePickingAuto._defineParams(self, form)
form.addParam('diameter',
params.IntParam,
default=100,
label='Diameter of particle in Å')
form.addParam('invert',
params.BooleanParam,
default=False,
label='Invert',
help="Invert image before picking, DoG normally picks "
"white particles.")
form.addParam('threshold',
params.FloatParam,
default=0.5,
label='Threshold',
help="Threshold in standard deviations above the mean, "
"e.g. --thresh=0.7")
form.addParam(
'extraParams',
params.StringParam,
expertLevel=params.LEVEL_ADVANCED,
label='Additional parameters',
help='Additional parameters for dogpicker: \n '
'--num-slices=, --size-range=, --max-thresh=, --max-area='
'--max-peaks=')
def _insertAllSteps(self):
self.inputStreaming = self.getInputMicrographs().isStreamOpen()
if self.streamingBatchSize > 0 or self.inputStreaming:
# If the input is in streaming, follow the base class policy
# about inserting new steps and discovery new input/output
ProtParticlePickingAuto._insertAllSteps(self)
self.createOutputStep = self._doNothing
else:
# If not in streaming, then we will just insert a single step to
# pick all micrographs at once since it is much faster
self._insertInitialSteps()
self._insertFunctionStep('_pickMicrographStep',
self.getInputMicrographs(),
*self._getPickArgs())
self._insertFunctionStep('createOutputStep')
# Disable streaming functions:
self._insertFinalSteps = self._doNothing
self._stepsCheck = self._doNothing
def _defineParams(self, form):
ProtParticlePickingAuto._defineParams(self, form)
form.addParam('modelInitialization', params.EnumParam,
choices=self.ADD_MODEL_TRAIN_TYPES,
default=self.ADD_MODEL_PRETRAINED,
label='Select model type',
help='If you set to *%s*, a topaz model object, '
'within this project, will be employed. If you set to *%s* a '
'pretrained model from topaz software will be used'
% tuple(self.ADD_MODEL_TRAIN_TYPES))
form.addParam('prevTopazModel', params.PointerParam,
pointerClass='TopazModel',
condition='modelInitialization== %s' % self.ADD_MODEL_PRETRAINED, allowsNull=True,
label='Select topaz model',
help='Select a topaz model to continue from.')
form.addParam('generalModel', params.EnumParam,
choices=self.GENERAL_MODELS, default=self.MODEL_RESNET16_U64,
condition='modelInitialization== %s' % self.ADD_MODEL_GENERAL,
label='Topaz general model',
help='A topaz NN model pretrained and provided in topaz sofware.'
'\nMight not be optimized for specific particles')
form.addSection('Picking')
form.addParam('radius', params.IntParam, default=8,
label='Particle radius (px)',
help='Pixel radius around particle centers to consider.')
form.addParam('boxSize', params.IntParam, default=-1, expertLevel=cons.LEVEL_ADVANCED,
label='Box size (px)', help='Box size in pixels. By default(-1): radius*2*scale')
form.addParam('threshold', params.FloatParam, default=-6.0,
label='Extraction threshold',
help='log-likelihood score threshold at which to terminate region extraction. '
'\nValue -6 is p>=0.0025 (default: -6)'
'\nHigher values will mean a more restrictive picking')
form.addParallelSection(threads=1, mpi=1)
self._definePreprocessParams(form)
self._defineStreamingParams(form)
form.getParam('streamingBatchSize').setDefault(32)
def _defineParams(self, form):
ProtParticlePickingAuto._defineParams(self, form)
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('pickType', params.EnumParam, default=0,
important=True,
label='Picking algorithm',
choices=['Ab-initio', 'Reference-based'],
display=params.EnumParam.DISPLAY_HLIST)
form.addParam('inputRefs', params.PointerParam,
condition='pickType==1', important=True,
pointerClass='SetOfClasses2D, SetOfAverages',
label='Input references',
help='Provide a set of 2D templates to use in '
'the search.')
form.addParam('maxradius', params.FloatParam, default=120.0,
label='Max particle radius (A)',
help='In Angstroms, the maximum radius of the '
'particles to be found. This also determines '
'the minimum distance between picks.')
form.addParam('radius', params.FloatParam, default=80.0,
label='Characteristic particle radius (A)',
help='In Angstroms, the radius within which most '
'of the density is enclosed. The template '
'for picking is a soft-edge disc, where '
'the edge is 5 pixels wide and this '
'parameter defines the radius at which '
'the cosine-edge template reaches 0.5.')
form.addParam('threshold', params.FloatParam, default=6.0,
label='Threshold peak height',
help='Particle coordinates will be defined as '
'the coordinates of any peak in the search '
'function which exceeds this threshold. '
'In numbers of standard deviations '
'above expected noise variations in '
'the scoring function. See Sigworth '
'(2004) for definition.')
form.addParam('avoidHighVar', params.BooleanParam, default=False,
label='Avoid high variance areas',
help='Avoid areas with abnormally high local variance. '
'This can be effective in avoiding edges '
'of support films or contamination.')
form.addParam('ptclWhite', params.BooleanParam, default=False,
label='Particles are white on a dark background?')
form.addSection(label='Expert options')
form.addParam('highRes', params.FloatParam, default=30.0,
label='Highest resolution used in picking (A)',
help='The template and micrograph will be resampled '
'(by Fourier cropping) to a pixel size of half '
'the resolution given here. Note that the '
'information in the corners of the Fourier '
'transforms (beyond the Nyquist frequency) '
'remains intact, so that there is some small '
'risk of bias beyond this resolution.')
form.addParam('minDist', params.IntParam, default=0,
label='Minimum distance from edges (px)',
help='No particle shall be picked closer than '
'this distance from the edges of the micrograph. '
'In pixels.')
form.addParam('useRadAvg', params.BooleanParam, default=True,
condition='pickType==1',
label='Use radial averages of templates',
help='Say yes if the templates should be '
'rotationally averaged')
form.addParam('rotateRef', params.IntParam, default=0,
condition='pickType==1 and not useRadAvg',
label='Rotate each template this many times',
help='If > 0, each template image will be '
'rotated this number of times and the '
'micrograph will be searched for the rotated '
'template.')
form.addParam('avoidLocMean', params.BooleanParam, default=True,
label='Avoid areas with abnormal local mean',
help='Avoid areas with abnormally low or high '
'local mean. This can be effective to avoid '
'picking from, e.g., contaminating ice crystals, '
'support film.')
form.addParam('bgBoxes', params.IntParam, default=30,
label='Number of background boxes',
help='Number of background areas to use in estimating '
'the background spectrum. The larger the number '
'of boxes, the more accurate the estimate should '
'be, provided that none of the background boxes '
'contain any particles to be picked.')
form.addParam('bgAlgo', params.EnumParam, default=LOW_VARIANCE,
choices=['Lowest variance', 'Variance near mode'],
display=params.EnumParam.DISPLAY_COMBO,
label='Algorithm to find background areas',
help='Testing so far suggests that areas of lowest '
'variance in experimental micrographs should be '
'used to estimate the background spectrum. '
'However, when using synthetic micrographs this '
'can lead to bias in the spectrum estimation '
'and the alternative (areas with local variances '
'near the mean of the distribution of local '
'variances) seems to perform better')
self._defineStreamingParams(form)
form.addParallelSection(threads=1, mpi=1)
def __init__(self, **kwargs):
ProtParticlePickingAuto.__init__(self, **kwargs)
self.stepsExecutionMode = STEPS_PARALLEL
def _defineParams(self, form):
ProtParticlePickingAuto._defineParams(self, form)
form.addParam('radius',
params.IntParam,
label='Radius of particle (px)')
form.addParam('height',
params.FloatParam,
default=0.5,
label="Min. height",
help='The minimum height of the virus peak compared to '
'the average peak.')
form.addParam('squareWidth',
params.FloatParam,
default=1.5,
label="Square width",
help='Minimum distance between two viruses is:\n'
'2 * SQUARE_WIDTH_PARAM * RADIUS.')
form.addParam('ringWidth',
params.FloatParam,
default=1.2,
label="Ring width",
help='Width of the ring in ring filter is: \n'
'RING_WIDTH_PARAM * RADIUS - RADIUS.')
form.addParam('dist',
params.FloatParam,
default=0.1,
label="Distance",
help='Distance which the peak can move in x or y '
'direction during center refinement is: \n'
'DIST_PARAM * RADIUS.')
form.addParam('reduction',
params.IntParam,
default=0,
label="Reduction",
help='REDUCTION_PARAM * REDUCTION_PARAM pixels are '
'averaged before filtering. The value has to be '
'integer. Special value 0 means that the program '
'determines the parameter.')
form.addParam('doRefine',
params.BooleanParam,
default=True,
expertLevel=LEVEL_ADVANCED,
label="Refine particle centers?",
help='')
form.addParam('doSectorTest',
params.BooleanParam,
default=True,
expertLevel=LEVEL_ADVANCED,
label="Perform sector test?",
help='')
form.addParam('doHeightTest',
params.BooleanParam,
default=True,
expertLevel=LEVEL_ADVANCED,
label="Perform height test?",
help='')
form.addParam('doDistanceTest',
params.BooleanParam,
default=True,
expertLevel=LEVEL_ADVANCED,
label="Perform peak pair distance test?",
help='')
def _createSetOfCoordinates(self, micSet, suffix=''):
""" Override this method to set the box size. """
coordSet = ProtParticlePickingAuto._createSetOfCoordinates(
self, micSet, suffix=suffix)
coordSet.setBoxSize(self.getBoxSize())
return coordSet
def _defineParams(self, form):
ProtParticlePickingAuto._defineParams(self, form)
form.addParam('inputModelFrom', params.EnumParam,
default=INPUT_MODEL_GENERAL,
choices=['general cryo (low-pass filtered)',
'general cryo (denoised)',
'other', 'general neg stain'],
display=params.EnumParam.DISPLAY_COMBO,
label='Picking model: ',
help="You might use a general network model that consists "
"of\n -cryo: real, simulated, particle free datasets on "
"various grids with contamination\n -negative stain: trained with"
"negative stain images\nand skip training "
"completely,\nor,\nif you would like to "
"improve the results you can use the model from a "
"previous training step or an imported one.")
form.addParam('inputModel', params.PointerParam,
allowsNull=True,
condition="inputModelFrom==%d" % INPUT_MODEL_OTHER,
label="Input model",
pointerClass='CryoloModel',
help='Select an existing crYOLO trained model.')
form.addParam('conservPickVar', params.FloatParam, default=0.3,
label="Confidence threshold",
help='If you want to pick less conservatively or more '
'conservatively you might want to change the threshold '
'from the default of 0.3 to a less conservative value '
'like 0.2 or more conservative value like 0.4.')
form.addParam('lowPassFilter', params.BooleanParam,
default=False,
label="Low-pass filter",
help="CrYOLO works on original micrographs but the "
"results will be probably improved by the application"
" of a reasonable low-pass filter.")
form.addParam('absCutOffFreq', params.FloatParam,
default=0.1,
condition='lowPassFilter',
label="Absolute cut off frequency",
help="Specifies the absolute cut-off frequency for the "
"low-pass filter.")
form.addParam('numCpus', params.IntParam, default=4,
label="Number of CPUs",
help="*Important!* This is different from number of threads "
"above as threads are used for GPU parallelization. "
"Provide here the number of CPU cores for each cryolo "
"process.")
form.addParam('input_size', params.IntParam,
default=1024,
expertLevel=cons.LEVEL_ADVANCED,
label="Input size",
help="This is the size to which the input is rescaled "
"before passed through the network."
"For example the default value would be 1024x1024.")
form.addParam('boxSize', params.IntParam,
default=0,
label='Box Size (optional)',
allowsPointers=True,
help='Box size in pixels. It should be the size of '
'the minimum particle enclosing square in pixel. '
'If introduced value is zero, it is estimated.')
form.addParam('max_box_per_image', params.IntParam,
default=600,
expertLevel=cons.LEVEL_ADVANCED,
label="Maximum box per image",
help="Maximum number of particles in the image. Only for "
"the memory handling. Keep the default value of "
"600 or 1000.")
form.addHidden(params.USE_GPU, params.BooleanParam, default=True,
expertLevel=params.LEVEL_ADVANCED,
label="Use GPU (vs CPU)",
help="Set to true if you want to use GPU implementation ")
form.addHidden(params.GPU_LIST, params.StringParam, default='0',
expertLevel=cons.LEVEL_ADVANCED,
label="Choose GPU IDs",
help="GPU may have several cores. Set it to zero"
" if you do not know what we are talking about."
" First core index is 0, second 1 and so on."
" crYOLO can use multiple GPUs - in that case"
" set to i.e. *0 1 2*.")
form.addParallelSection(threads=1, mpi=1)
self._defineStreamingParams(form)
# Default batch size --> 16
form.getParam('streamingBatchSize').default = Integer(16)
def __init__(self, **args):
ProtParticlePickingAuto.__init__(self, **args)
def _defineParams(self, form):
ProtParticlePickingAuto._defineParams(self, form)
form.addHidden(USE_GPU, BooleanParam, default=False,
label="Use GPU?",
help="Set to Yes if you want to run Neural Net "
"boxer on GPU. Default is CPU.")
form.addHidden(GPU_LIST, StringParam, default='0',
label="Choose GPU ID",
help="GPU may have several cores. Set it to zero"
" if you do not know what we are talking about."
" First core index is 0, second 1 and so on.\n"
"Eman boxer can use only one GPU.")
form.addParam('boxSize', IntParam, default=128,
label='Box size (px)',
help="Box size in pixels.")
form.addParam('particleSize', IntParam, default=100,
label='Particle size (px)',
help="Longest axis of particle in pixels (diameter, "
"not radius).")
form.addParam('boxerMode', EnumParam,
choices=['local search', 'by ref', 'neural net', 'gauss'],
label="Autopicker mode:", default=AUTO_CONVNET,
display=EnumParam.DISPLAY_COMBO,
help="Choose autopicker mode:\n\n"
" _local search_ - Reference based search by "
"downsampling and 2-D alignment to references.\n"
" _by ref_ - simple reference-based "
"cross-correlation picker with exhaustive "
"rotational search.\n"
" _neural net_ - convolutional neural network "
"boxer.\n"
" _gauss_ - simple reference-free picker.")
form.addParam('threshold', FloatParam, default='5.0',
label='Threshold',
condition='boxerMode!=%d' % AUTO_GAUSS)
form.addParam('threshold2', FloatParam, default='-5.0',
condition='boxerMode==%d' % AUTO_CONVNET,
label='Threshold2')
form.addParam('gaussLow', FloatParam, default=1.,
condition='boxerMode==%d' % AUTO_GAUSS,
label='Threshold low')
form.addParam('gaussHigh', FloatParam, default=2.,
condition='boxerMode==%d' % AUTO_GAUSS,
label='Threshold high')
form.addParam('gaussWidth', FloatParam, default=1.,
condition='boxerMode==%d' % AUTO_GAUSS,
label='Gaussian width')
form.addSection('References')
form.addParam('boxerProt', PointerParam,
pointerClass='EmanProtBoxing',
condition='boxerMode==%d' % AUTO_CONVNET,
label='Previous e2boxer protocol',
help='Provide previously executed e2boxer protocol '
'that has all 3 types of references and '
'pre-trained neural network.')
form.addParam('goodRefs', PointerParam,
pointerClass='SetOfAverages',
condition='boxerMode<%d' % AUTO_CONVNET,
allowsNull=True,
label="Good references",
help="Good particle references.")
form.addParallelSection(threads=1, mpi=0)
def getFiles(self):
return (self.inputMicrographs.get().getFiles() |
ProtParticlePickingAuto.getFiles(self))
