EMAN2 Averager Manual

Last modified on Tue, 05 Apr 2022 00:12:57 CDT
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Averager Name Parameters Description

ctf.auto Averaging with automatic CTF correction and SNR weight. No B-factor correction (as this is best done in 3-D). Bases estimated SSNR on CTF parameters, so requires EMAN2 CTF parameters.

ctf.weight Average without CTF correction but with CTF weighting. Smoothed SNR can still have large uncertainty, so weighting by envelope-free CTF may provide more uniform results.

ctf.weight.autofilt Average without CTF correction but with CTF weighting and automatic filter estimated from the data. Smoothed SNR can still have large uncertainty, so weighting by envelope-free CTF may provide more uniform results.

ctfw.auto Averaging with autmatic CTF correction. Does not require a structure factor, but only works with EMAN2's CTF model

iterative EXPERIMENTAL! Not suggested for normal use. An iterative averager making use of local correlations for noise reduction

localweight float dampnoise: Will set a minimum mean*x for the norm image, damping regions with poor information. Default = 0.5, 0 disables.
int fourier: If set does local weighting in Fourier rather than real space
emdata normimage: After finish() will contain the sum of the weights in real-space
Average of images weighted by local similarity to unweighted average

mean int ignore0: if set, ignore zero value pixels
emdata normimage: In conjunction with ignore0, the number of non zero values for each pixel will be stored in this image.
emdata sigma: sigma value
Simple mean average of images

mean.tomo int doift: IFT the resulting volume. Default is 1.
emdata normout: If set, will save the normalization volume in the given EMData object.
int save_norm: If set, will save the normalization volume as norm.hdf. Mainly for debugging purposes.
float thresh_sigma: multiplied by the standard deviation of the image, below-which values are considered zero. Default = .01
Average of volumes in Fourier space, excluding any pixels with near 0 intensity.

median Computes the median value instead of the mean. If even number of images, averages the middle two.

minmax int abs: If set, will find the value with the min or max absolute value. The actual value is preserved.
int max: If set, will find the max value, otherwise finds min
emdata owner: Contains the number of the input image which 'owns' the max/min value. Value will be insertion sequence number unless 'ortid' is set in each image being averaged.
Finds the minimum or maximum value in each pixel

sigma int ignore0: if set, ignore zero value pixels
emdata normimage: In conjunction with ignore0, the number of non zero values for each pixel will be stored in this image.
Computes the standard deviation of images

weightedfourier emdata normimage: After finish() will contain the sum of the weights in each Fourier location. Size must be ((nx+1)/2,y)
Weighted mean of images in Fourier space. Each image must have weighting curve in its header, an XYData object called 'avg_weight'.

Averager Name	Parameters	Description
ctf.auto		Averaging with automatic CTF correction and SNR weight. No B-factor correction (as this is best done in 3-D). Bases estimated SSNR on CTF parameters, so requires EMAN2 CTF parameters.
ctf.weight		Average without CTF correction but with CTF weighting. Smoothed SNR can still have large uncertainty, so weighting by envelope-free CTF may provide more uniform results.
ctf.weight.autofilt		Average without CTF correction but with CTF weighting and automatic filter estimated from the data. Smoothed SNR can still have large uncertainty, so weighting by envelope-free CTF may provide more uniform results.
ctfw.auto		Averaging with autmatic CTF correction. Does not require a structure factor, but only works with EMAN2's CTF model
iterative		EXPERIMENTAL! Not suggested for normal use. An iterative averager making use of local correlations for noise reduction
localweight	float dampnoise: Will set a minimum meanx for the norm image, damping regions with poor information. Default = 0.5, 0 disables. int fourier: If set does local weighting in Fourier rather than real space emdata normimage*: After finish() will contain the sum of the weights in real-space	Average of images weighted by local similarity to unweighted average
mean	int ignore0: if set, ignore zero value pixels emdata normimage: In conjunction with ignore0, the number of non zero values for each pixel will be stored in this image. emdata sigma: sigma value	Simple mean average of images
mean.tomo	int doift: IFT the resulting volume. Default is 1. emdata normout: If set, will save the normalization volume in the given EMData object. int save_norm: If set, will save the normalization volume as norm.hdf. Mainly for debugging purposes. float thresh_sigma: multiplied by the standard deviation of the image, below-which values are considered zero. Default = .01	Average of volumes in Fourier space, excluding any pixels with near 0 intensity.
median		Computes the median value instead of the mean. If even number of images, averages the middle two.
minmax	int abs: If set, will find the value with the min or max absolute value. The actual value is preserved. int max: If set, will find the max value, otherwise finds min emdata owner: Contains the number of the input image which 'owns' the max/min value. Value will be insertion sequence number unless 'ortid' is set in each image being averaged.	Finds the minimum or maximum value in each pixel
sigma	int ignore0: if set, ignore zero value pixels emdata normimage: In conjunction with ignore0, the number of non zero values for each pixel will be stored in this image.	Computes the standard deviation of images
weightedfourier	emdata normimage: After finish() will contain the sum of the weights in each Fourier location. Size must be ((nx+1)/2,y)	Weighted mean of images in Fourier space. Each image must have weighting curve in its header, an XYData object called 'avg_weight'.