xmipp3.protocols.protocol_screen_deepConsensus module

Deep Consensus picking protocol

class xmipp3.protocols.protocol_screen_deepConsensus.XmippProtDeepConsSubSet(**args)

Bases: pwem.protocols.protocol_batch.ProtUserSubSet

Create subsets from the GUI for the Deep Consensus protocol. This protocol will be executed mainly calling the script ‘pw_create_image_subsets.py’ from the ShowJ gui. The enabled/disabled changes will be stored in a temporary sqlite file that will be read to create the new subset.

class xmipp3.protocols.protocol_screen_deepConsensus.XmippProtScreenDeepConsensus(**args)

Bases: pwem.protocols.protocol_particles_picking.ProtParticlePicking, xmipp3.base.XmippProtocol

Protocol to compute a smart consensus between different particle picking algorithms. The protocol takes several Sets of Coordinates calculated by different programs and/or different parameter settings. Let’s say: we consider N independent pickings. Then, a neural network is trained using different subset of picked and not picked cooridantes. Finally, a coordinate is considered to be a correct particle according to the neural network predictions.

ADD_DATA_TRAIN_CUST = 2

ADD_DATA_TRAIN_CUSTOM_OPT = ['Particles', 'Coordinates']

ADD_DATA_TRAIN_CUSTOM_OPT_COORS = 1

ADD_DATA_TRAIN_CUSTOM_OPT_PARTS = 0

ADD_DATA_TRAIN_NONE = 0

ADD_DATA_TRAIN_PRECOMP = 1

ADD_DATA_TRAIN_TYPES = ['None', 'Precompiled', 'Custom']

ADD_MODEL_TRAIN_NEW = 0

ADD_MODEL_TRAIN_PRETRAIN = 1

ADD_MODEL_TRAIN_PREVRUN = 2

ADD_MODEL_TRAIN_TYPES = ['New', 'Pretrained', 'PreviousRun']

CONSENSUS_COOR_PATH_TEMPLATE = 'consensus_coords_%s'

CONSENSUS_PARTS_PATH_TEMPLATE = 'consensus_parts_%s'

PRE_PROC_MICs_PATH = 'preProcMics'

calculateCoorConsensusStep(outCoordsDataPath, mode)

checkIfPrevRunIsCompatible(inputType='')

inputType can be mics_ or coords_ or “”

createOutputStep()

extractParticles(mode)

getMicsIds(filterOutNoCoords=False)

getPreProcParamsFromForm()

initializeStep()

Create paths where data will be saved

insertCaculateConsensusSteps(mode, prerequisites)

insertExtractPartSteps(mode, prerequisites)

joinSetOfParticlesStep(mode)

justPredict()

loadCoords(posCoorsPath, mode)

pickNoise()

predictCNN()

preprocessMicsStep()

retrieveTrainSets()

Retrieve, link and return a setOfParticles corresponding to the NegativeTrain DeepConsensus trainning set with certain extraction conditions (phaseFlip/invContrast)

trainCNN()