xmipp3.protocols.protocol_classify_pca module

class xmipp3.protocols.protocol_classify_pca.XMIPPCOLUMNS(value)

Bases: Enum

An enumeration.

anglePsi = 'anglePsi'

angleRot = 'angleRot'

angleTilt = 'angleTilt'

classCount = 'classCount'

ctfCritFitting = 'ctfCritFitting'

ctfCritMaxFreq = 'ctfCritMaxFreq'

ctfDefocusAngle = 'ctfDefocusAngle'

ctfDefocusU = 'ctfDefocusU'

ctfDefocusV = 'ctfDefocusV'

ctfQ0 = 'ctfQ0'

ctfSphericalAberration = 'ctfSphericalAberration'

ctfVoltage = 'ctfVoltage'

enabled = 'enabled'

flip = 'flip'

image = 'image'

itemId = 'itemId'

micrograph = 'micrograph'

micrographId = 'micrographId'

ref = 'ref'

scoreByGiniCoeff = 'scoreByGiniCoeff'

scoreByVariance = 'scoreByVariance'

shiftX = 'shiftX'

shiftY = 'shiftY'

shiftZ = 'shiftZ'

xcoor = 'xcoor'

ycoor = 'ycoor'

class xmipp3.protocols.protocol_classify_pca.XmippProtClassifyPca(**args)

Bases: ProtClassify2D, XmippProtocol

Classifies a set of images.

CREATE_CLASSES = 0

UPDATE_CLASSES = 1

classification(inputIm, numClass, stfile, mask, sigma)

convertInputStep(input, outputOrig, outputMRC)

createOutputAverages(outputClasses)

createOutputStep()

Store the SetOfClasses2D object resulting from the protocol execution.

pcaTraining(inputIm, resolutionTrain, numTrain)

xmipp3.protocols.protocol_classify_pca.rowToAlignmentEmtable(alignmentRow, alignType)

is2D == True-> matrix is 2D (2D images alignment)

otherwise matrix is 3D (3D volume alignment or projection)

invTransform == True -> for xmipp implies projection

xmipp3.protocols.protocol_classify_pca.updateEnviron(gpuNum)

Create the needed environment for pytorch programs.