The “PysofiData()” class

The PysofiData class is the fundamental data container that carries the measurement data and pysofi-related properties and behaviors. User-defined parameters and intermediate results are bundled to the PysofiData object as attributes as well as processing steps as methods. The full SOFI 2.0 analysis pipeline can be achieved based on multiple PysofiData methods. Each processing step is connected to a separate outside module supported by pysofi, and can be utilized not only by pysofi, but also by other computational imaging developers for similar purposes.

Attributes and Methods

class pysofi.pysofi.PysofiData(filepath, filename)

Bases: object

Data object contains the information of a dataset (e.g. dimensions, frame numbers), and provides SOFI methods to perform analysis and visualization on the dataset (moments reconstruction, cumulants reconstruction, shrinking kernel deconvolution, etc…).

When loading a tiff file, a PysofiData() object is created and further SOFI analysis can be preformed. All the standard data-attributes are listed below. New data-attributes can be added or updated using ‘.add()’ function.

Parameters

  • filapath (str) – Path to the tiff file.

  • filename (str) – Name of the tiff file.

filename
Type

str

filepath
Type

str

ave

The average image of the image stack / tiff video.

Type

ndarray

finterp_factor

The interpolation factor for Fourier interpolation.

Type

int

morder_lst

All orders of moments-reconstructions that have been calculated.

Type

list

morder_finterp_lst

All orders of moments-reconstructions after Fourier interpolation that have been calculated.

Type

list

moments_set

moment order (int) -> moment-reconstructed image (ndarray) A dictionary of orders and corrensponding moment reconstructions.

Type

dict

moments_set_bc

moment order (int) -> moment-reconstructed image (ndarray) A dictionary of orders and corrensponding moment reconstructions with bleaching correction.

Type

dict

cumulants_set

cumulant order (int) -> cumulant-reconstructed image (ndarray) A dictionary of orders and corrensponding cumulant reconstructions.

Type

dict

cumulants_set_bc

cumulant order (int) -> cumulant-reconstructed image (ndarray) A dictionary of orders and corrensponding cumulant reconstructions with bleaching correction.

Type

dict

morder

The highest order of moment reconstruction that has been calculated.

Type

int

corder

The highest order of cumulant reconstruction that has been calculated.

Type

int

fbc

Bleaching correction factor.

Type

float

n_frames

The number of frames of the image stack / tiff video.

Type

int

xdim

The number of pixels in x dimension.

Type

int

ydim

The number of pixels in y dimension.

Type

int

Notes

For SOFI processing, after loading the tiff video into the Data object, a pipeline of 1) fourier interpolation, 2) moments reconstrtuction or cumulants reconstruction, 3) noise filtering 1, 4) shrinking kernel de- convolution, 5) noise filtering 2, and 6) ldrc. The processed video will be saved into a new tiff file with the colormap user selects.

References

1

Xiyu Yi, Sungho Son, Ryoko Ando, Atsushi Miyawaki, and Shimon Weiss,

“Moments reconstruction and local dynamic range compression of high order superresolution optical fluctuation imaging,” Biomed. Opt. Express 10, 2430-2445 (2019).

add(**kwargs)

Add or update elements (attributes and/or dict entries).

add_filtered(image, filter_name='noise filter')

Add (noise) filtered image as an attribute of the object.

average_image(frames=[])

Calculate average image of the tiff video.

average_image_with_finterp(interp_num)
bleach_correct(fbc=0.04, smooth_kernel=251, save_option=True, return_option=False)

Performs bleaching correction on a tiff image (stack) with a bleaching correction factor.

Parameters

  • fbc (float) – The fraction of signal decrease within each block compared to the total signal decrease. Only used when bleach correction is True.

  • smooth_kernel (int) – The size of the median filter window.

  • save_option (bool) – Whether to save the corrected images into tiff files.

  • return_option (bool) – Whether to return the corrected image series as a 3d array.

Returns

bc_im – All bleaching-corrected imagee in a 3d array.

Return type

ndarray

calc_moments_set(highest_order=4, frames=[], mean_im=None, bleach_correction=False, smooth_kernel=251, fbc=0.04)

Calculate moment-reconstructed images to the highest order.

Parameters

  • highest_order (int) – The highest order number of moment-reconstructed images.

  • frames (list of int) – The start and end frame number.

  • mean_im (ndarray) – Average image of the tiff stack.

  • bleach_correction (bool) – Whether to use bleaching correction.

  • smooth_kernel (int, optional) – The size of the median filter window. Only used when bleach correction is True.

  • fbc (float, optional) – The fraction of signal decrease within each block compared to the total signal decrease. Only used when bleach correction is True.

Returns

moments_set, moments_set_bc – order number (int) -> image (ndarray) A dictionary of calcualted moment-reconstructed images.

Return type

dict

cumulants_images(highest_order=4, frames=[], m_set=None, bleach_correction=False, smooth_kernel=251, fbc=0.04)

Calculate cumulant-reconstructed images to the highest order.

Parameters

  • highest_order (int) – The highest order number of cumulant-reconstructed images.

  • frames (list of int) – The start and end frame number.

  • m_set (dict) – order number (int) -> image (ndarray) A dictionary of calcualted moment-reconstructed images.

  • bleach_correction (bool) – Whether to use bleaching correction.

  • smooth_kernel (int, optional) – The size of the median filter window. Only used when bleach correction is True.

  • fbc (float, optional) – The fraction of signal decrease within each block compared to the total signal decrease. Only used when bleach correction is True.

Returns

cumulants_set, cumulants_set_bc – order number (int) -> image (ndarray) A dictionary of calcualted cumulant-reconstructed images.

Return type

dict

deconvsk(est_psf=array([[0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], ..., [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0]]), input_im=None, deconv_lambda=1.5, deconv_iter=20)

Perform serial Richardson-Lucy deconvolution with a series of PSFs on the input image. For details of shrinking kernel deconvolution, refer to deconvsk.py and [1].

Parameters

  • est_psf (ndarray) – Estimated PSF.

  • input_im (ndarray) – Input image that need deconvolution.

  • deconv_lambda (float) – Lambda for the exponent between. It is an empirical parameter within the range of (1,2).

  • deconv_iter (int) – Number of iterations for each deconvolution.

Returns

deconv – Deconvoluted image.

Return type

ndarray

finterp_image(input_im=None, interp_num_lst=[2, 4])

Performs fourier interpolation on an image array.

finterp_tiffstack(interp_num_lst=[2, 4], frames=[], save_option=True, return_option=False)

Performs fourier interpolation on a tiff image (stack) with a list of interpolation factors (the number of pixels to be interpolated between two adjacent pixels).

Parameters

  • interp_num_lst (list (int)) – A list of interpolation factors.

  • frames (list of int) – The start and end frame number.

  • save_option (bool) – Whether to save the interpolated images into tiff files (each interpolation factor seperately).

  • return_option (bool) – Whether to return the interpolated image series as 3d arrays.

Returns

finterps – A list of interpolated image sereis corresponding to the interpolation factor list.

Return type

list (ndarray)

get_dims()

Get dimensions and frame number of the tiff video.

get_frame(frame_num=0)

Get one frame of the tiff video.

Parameters

frame_num (int) – The frame number (e.g. 0 for the first frame).

Returns

frame_im – Frame array.

Return type

ndarray

ldrc(order=6, window_size=[25, 25], mask_im=None, input_im=None)

Compress the dynamic range of moments-/cumulants reconstruced image with ldrc method. For details of ldrc, refer to ldrc.py and [1].

Parameters

  • order (int) – The order of the reconstructed image.

  • window_size ([int, int]) – The [x, y] dimension of the scanning window.

  • mask_im (ndarray) – A reference image. Usually a average/sum image or second-order SOFI image is used.

  • input_im (ndarray) – An input image, usually a high-order moment- or cumulant- reconstructed image.

Returns

ldrc_image – The compressed image with the same dimensions of input_im.

Return type

ndarray

moment_image(order=6, mean_im=None, mvlength=[], finterp=False, interp_num=1)

Calculate the moment-reconstructed image of a defined order.

Parameters

  • order (int) – The order number of the moment-reconstructed image.

  • mean_im (ndarray) – Average image of the tiff stack.

  • mvlength (int) – Length of the video to calculate moments-reconstruction.

  • finterp (bool) – Whether to first conduct Fourier interpolation then calculate moments-reconstructions.

  • interp_num (int) – The interpolation factor.

Returns

moment_im – The calculated moment-reconstructed image.

Return type

ndarray

Load Data

The simulated or experimental measurements can be loaded into the pysofi.PysofiData object using:

d = pysofi.PysofiData(filepath, filename)