xmipp3.protocols.protocol_screen_deepConsensus module

Deep Consensus picking protocol

class xmipp3.protocols.protocol_screen_deepConsensus.XmippProtDeepConsSubSet(**args)[source]

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)[source]

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)[source]
checkIfPrevRunIsCompatible(inputType='')[source]

inputType can be mics_ or coords_ or “”

createOutputStep()[source]
extractParticles(mode)[source]
getMicsIds(filterOutNoCoords=False)[source]
getPreProcParamsFromForm()[source]
initializeStep()[source]

Create paths where data will be saved

insertCaculateConsensusSteps(mode, prerequisites)[source]
insertExtractPartSteps(mode, prerequisites)[source]
joinSetOfParticlesStep(mode)[source]
justPredict()[source]
loadCoords(posCoorsPath, mode)[source]
pickNoise()[source]
predictCNN()[source]
preprocessMicsStep()[source]
retrieveTrainSets()[source]

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

trainCNN()[source]