e2gmm_refine

usage: Single particle alignment, reconstruction and heterogeneity analysis with Gaussian model and neural networks. There are a few modes.
	
	Reconstruction from projections or particles with transforms:
	e2gmm_refine.py --projs <projection file> --modelout <model output text file> --npts <number of Gaussian in model>
	
	Align particles using the model (local refinement only, still under testing):
	e2gmm_refine.py --model <model input> --ptclsin <particle input list> --ptclout <particle list output> --align 
	
	Heterogeneity analysis:
	e2gmm_refine.py --model <model input> --ptclsin <particle input list>  --heter --midout <conformation output text file>

Option	Type	Description
--version	None	show program's version number and exit
--sym	str	symmetry. currently only support c and d
--model	str	load from an existing model file
--modelout	str	output trained model file. only used when --projs is provided
--decoderin	str	Rather than initializing the decoder from a model, read an existing trained decoder
--decoderout	str	Save the trained decoder model. Filename should be .h5
--projs	str	projections with orientations (in hdf header or comment column of lst file) to train model
--evalmodel	str	generate model projection images to the given file name
--evalsize	int	Box size for the projections for evaluation.
--ptclsin	str	particles input for alignment
--ptclsout	str	aligned particle output
--learnrate	float	learning rate for model training only. Default is 1e-4.
--sigmareg	float	regularizer for the sigma of gaussian width. Larger value means all Gaussian functions will have essentially the same width. Smaller value may help compensating local resolution difference.
--modelreg	float	regularizer for for Gaussian positions based on the starting model, ie the result will be biased towards the starting model when training the decoder (0-1 typ). Default 0
--ampreg	float	regularizer for the Gaussian amplitudes in the first 1/2 of the iterations. Large values will encourage all Gaussians to have similar amplitudes. default = 0
--niter	int	number of iterations
--npts	int	number of points to initialize.
--batchsz	int	batch size
--maxboxsz	int	maximum fourier box size to use. 2 x target Fourier radius.
--maxres	float	maximum resolution. will overwrite maxboxsz.
--align	None	align particles.
--heter	None	heterogeneity analysis.
--decoderentropy	None	This will train some entropy into the decoder using particles to reduce vanishing gradient problems
--perturb	float	Relative perturbation level to apply in each iteration during --heter training. Default = 0.1, decrease if models are too disordered
--conv	None	Use a convolutional network for heterogeneity analysis.
--fromscratch	None	start from coarse alignment. otherwise will only do refinement from last round
--gradout	str	gradient output
--gradin	str	reading from gradient output instead of recomputing
--midout	str	middle layer output
--pas	str	choose whether to adjust position, amplitude, sigma. use 3 digit 0/1 input. default is 110, i.e. only adjusting position and amplitude
--nmid	int	size of the middle layer
--ndense	int	size of the layers between the middle and in/out, variable if -1. Default 512
--mask	str	remove points outside mask
--ppid	int	Set the PID of the parent process, used for cross platform PPID