# Name

recons3d_nn_SSNR - calcuate signal noise ratio & variance of 3D reconstruction using nearest neighbor interpolation.

# Usage

- [ssnr, vol_ssnr] = recons3d_nn_SSNR(stack_name, list_proj, symmetry, npad, snr, sign, w, filename)

## Input

- stack_name
- Name of the file with projection data
- list_proj
- Index of the projection which will be utilized
- ctf
- flag for turning on ctf SSNR calculation. In order to correct ctf, ctf SSNR calculation would first calculate 3D Wiener summation of all the raw images, and then calculate SSNR.
- symmetry
- Point group of the target molecule, default to "c1"
- npad
- Times of padding to be applied, default to be 1
- snr
- signal noise ratio of the image, used for ctf SSNR calculation.
- sign
- sign of ctf.
- w
- The thickness of the shell, default to be 1
- filename
- The filename in which you wish to store the SSNR value, default to be None

## Output

- 3D reconstructed volume image.
- ssnr file with 3 columns: 1. frequencies; 2. signal noise ratio; 3. variance; 4. number of images. 5. radius square
- outlist: a list containing two columns: 1. frequencies 2. variances
- vol_ssnr: SSNR 3D volume,which is real and has Fourier volume dimension. It can be multiplied to Fourier volume.

# Description

- Weighted nearest neighbour algorithm is used in the 3D reconstruction of SSNR calculation.
- ctf SSNR requires that the images are not pre-multiplied by ctf.

# Method

# Reference

# Author / Maintainer

Zhengfan Yang& Zhong Huang

# Keywords

- category 1
- INVERSION
- category 2
- FOURIER

# Files

reconstruction.py

# See also

# Maturity

- stable
- works for most people, has been tested; test cases/examples available.

# Bugs

None. It is perfect.