In the end of each demo notebook, it listed all the output parameters obtained from PSF learning. Here we provide the description of those parameters from different imaging modalities.
| Parameters | Description |
|---|---|
| locres | localization results of the data used for learning |
| CRLB | CRLB, theoretical localization variance of each variable |
| LL | Loglikelihood ratio of each emitter |
| loc | Estimated positions, unit: pixel |
| coeff | Spline coefficients used for spline based localization algorithm |
| coeff_reverse | Same as coeff but with z dimension reversed |
| coeff_bead | Same as coeff but only for localizing bead data |
| res | PSF learning results |
| I_model | Learned PSF model for modelling single molecules, a 3D matrix |
| I_model_reverse | Same as I_model but with z dimension reversed |
| I_model_bead | Learned PSF model for modelling bead data |
| bg | Learned background values of each emitter |
| intensity | Learned total photon count of each emitter |
| pos | Learned x,y,z positions of each emitter, unit: pixel |
| pupil | Learned pupil function, a 2D complex matrix |
| zernike_coeff | Learned Zernike coefficients of the pupil function, including both the coefficients for pupil magnitude and pupil phase |
| sigma | Learned widths in x,y of the Gaussian blurring kernel, unit: pixel |
| drift_rate | Learned x,y drift for each bead stack, unit: pixel per z slice |
| cor | Pixel coordinates of final emitters |
| cor_all | Pixel coordinates of all candidate emitters |
| apodization | The apodization term of the pupil, a 2D matrix |
| zernike_polynomials | The matrix representation of each Zernike polynomials used in learning, a set of 2D matrices |
| offset | The minimum value of I_model, ideally it should be greater than zero |
| rois | |
| cor | Pixel coordinates of final emitters |
| fileID | Data file No. of final emitters |
| image_size | The image size of the raw data, unit: pixel |
| psf_data | The selected rois of final emitters |
| psf_fit | The PSF models of final emitters, same size as psf_data |
Below list parameters that are different from single channel
| Parameters | Description |
|---|---|
| res | PSF learning results |
| T | Affine transformation matrix between each target channel to the reference channel, a stack of 3x3 matrices |
| channelN | Learned results from Nth channel, see res in single channel, N counts from 0. |
| imgcenter | The pixel coordinate of the image center from the raw data, it defines the rotation center of T |
| xyshift | The initial estimation of the lateral shift between the target channel to the reference channel, unit: pixel |
The first level output parameters are the same as the ones in multi-channel, however the parameters in channelN are different from the ones in single channel, below list the difference.
| Parameters | Description |
|---|---|
| channelN | Learned results from Nth channel |
| I_model | Learned model for matrix I in the IAB model, a 3D matrix |
| A_model | Learned model for matrix A and B in the IAB model, a complex 3D matrix |
| I_model_reverse | Same as I_model but with z dimension reversed |
| A_model_reverse | Same as A_model but with z dimension reversed |
| intensity | Learned total photon (real(intensity)) and interference phase (angle(intensity)) of each emitter, a complex vector |
| phase_dm | Learned relative phases of the three axial scans in one dataset, a vector of three values |
| pupil1 | Learned pupil function of the top emission path, a 2D complex matrix |
| pupil2 | Learned pupil function of the bottom emission path, a 2D complex matrix |
| zernike_coeff_mag | Learned Zernike coefficients of the magnitude parts of pupil1 and pupil2
|
| zernike_coeff_phase | Learned Zernike coefficients of the phase parts of pupil1 and pupil2
|
| modulation_depth | Learned modulation depth, defines the weight factor of the coherent part of the PSF model |
| offset | The minimum value of the PSF model, ideally it should be greater than zero. In IAB model, the PSF model at interference phase equal to zero is |
| Zphase | The stage position (in pixels) multiplied by |
Below list parameters that are different from single channel
| Parameters | Description |
|---|---|
| locres | localization results of the data used for learning |
| others | Corresponding values from averaged PSF model |
| loc_FD | Estimated positions from the PSF model for each emitter |
| res | PSF learning results |
| I_model_all | Learned PSF model for each emitter, a set of 3D matrices |
| I_model_bead | Learned averaged PSF model for modelling bead data, a 3D matrix |
| I_model | Learned averaged PSF model for modelling single molecules, a 3D matrix |
| pupil | Learned pupil function of each emitter, a set of 2D complex matrix |
| zernike_coeff | Learned Zernike coefficients of the pupil function of each emitter, including both the coefficients for pupil magnitude and pupil phase. A set of 2D arrays |
| zernike_map | Learned aberration maps of both the pupil magnitude and pupil phase for each Zernike polynomial |
Below list additional parameters from in situ learning
| Parameters | Description |
|---|---|
| res or res/channelN | PSF learning results |
| stagepos | Learned stage position, a positive scalar, unit: micron |
| zoffset | z position of the first slice in the learned PSF model, unit: pixel |
| sampleheight | Learned thickness of the sample chamber in a 4Pi system, unit: micron |