xmipp3.protocols.protocol_screen_deepConsensus module¶
Deep Consensus picking protocol
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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.
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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.
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ADD_DATA_TRAIN_CUST
= 2¶
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ADD_DATA_TRAIN_CUSTOM_OPT
= ['Particles', 'Coordinates']¶
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ADD_DATA_TRAIN_CUSTOM_OPT_COORS
= 1¶
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ADD_DATA_TRAIN_CUSTOM_OPT_PARTS
= 0¶
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ADD_DATA_TRAIN_NONE
= 0¶
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ADD_DATA_TRAIN_PRECOMP
= 1¶
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ADD_DATA_TRAIN_TYPES
= ['None', 'Precompiled', 'Custom']¶
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ADD_MODEL_TRAIN_NEW
= 0¶
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ADD_MODEL_TRAIN_PRETRAIN
= 1¶
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ADD_MODEL_TRAIN_PREVRUN
= 2¶
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ADD_MODEL_TRAIN_TYPES
= ['New', 'Pretrained', 'PreviousRun']¶
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CONSENSUS_COOR_PATH_TEMPLATE
= 'consensus_coords_%s'¶
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CONSENSUS_PARTS_PATH_TEMPLATE
= 'consensus_parts_%s'¶
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PRE_PROC_MICs_PATH
= 'preProcMics'¶
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