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 using Principal Component Analysis (PCA). This 2D classification groups (the number of groups can be set) are based on their similarities, assisting in the identification of different conformational states or particle populations.

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.

getGpusList(separator)

pcaTraining(inputIm, resolutionTrain, numTrain)

setGPU(oneGPU=False)

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