nanopyx.methods.esrrf_3d.eSRRF3D_workflow

  1from ..workflow import Workflow
  2from ...core.transform._le_esrrf3d import eSRRF3D as eSRRF3D_ST
  3from ...core.transform.mpcorrector import macro_pixel_corrector
  4
  5import numpy as np
  6
  7
  8def eSRRF3D(
  9    img,
 10    magnification_xy=2,
 11    magnification_z=2,
 12    radius: float = 1.5,
 13    PSF_ratio: float = 2.8,
 14    voxel_ratio: float = 4.0,
 15    sensitivity: float = 1,
 16    frames_per_timepoint: int = 0,
 17    mode: str = "average",
 18    doIntensityWeighting: bool = True,
 19    macro_pixel_correction: bool = True,
 20    _force_run_type=None,
 21):
 22    """
 23    Perform eSRRF3D analysis on an image.
 24
 25    Args:
 26        img (numpy.ndarray): The input image for eSRRF3D analysis.
 27        magnification_xy (int, optional): Magnification factor in XY plane (default is 2).
 28        magnification_z (int, optional): Magnification factor in Z plane (default is 2).
 29        radius (float, optional): Radius parameter for eSRRF3D analysis (default is 1.5).
 30        PSF_ratio (float, optional): Ratio of PSF shape as Z/XY for eSRRF3D analysis (default is 2.8).
 31        voxel_ratio (float, optional): Ratio of voxel size in XY to Z direction (default is 4.0).
 32        frames_per_timepoint (int, optional): Number of frames per timepoint (default is 0, which means all frames are used).
 33        sensitivity (float, optional): Sensitivity parameter for eSRRF3D analysis (default is 1).
 34        mode (str, optional): Time projection mode (default is "average").
 35        doIntensityWeighting (bool, optional): Enable intensity weighting (default is True).
 36        macro_pixel_correction (bool, optional): Enable macro pixel correction (default is True).
 37        _force_run_type (str, optional): Force a specific run type for the analysis (default is None).
 38
 39    Returns:
 40        numpy.ndarray: The result of eSRRF3D analysis, typically representing the localizations.
 41
 42    Example:
 43        result = eSRRF3D(image, magnification_xy=2, magnification_z=2, radius=1.5, sensitivity=1, doIntensityWeighting=True)
 44
 45    Note:
 46        - eSRRF3D (enhanced Super-Resolution Radial Fluctuations 3D) is a method for super-resolution localization microscopy in three dimensions.
 47        - This function sets up a workflow to perform eSRRF3D analysis on the input image.
 48
 49    See Also:
 50        - eSRRF3D_ST: The eSRRF3D step that performs the actual analysis.
 51        - Workflow: The class used to define and run analysis workflows.
 52
 53    """
 54
 55    if frames_per_timepoint == 0:
 56        frames_per_timepoint = img.shape[0]
 57    elif frames_per_timepoint > img.shape[0]:
 58        frames_per_timepoint = img.shape[0]
 59
 60    number_of_timepoints = img.shape[0] // frames_per_timepoint
 61    if img.shape[0] % frames_per_timepoint != 0:
 62        number_of_timepoints += 1
 63
 64    output_array = np.zeros(
 65        (
 66            number_of_timepoints,
 67            img.shape[1] * magnification_z,
 68            img.shape[2] * magnification_xy,
 69            img.shape[3] * magnification_xy,
 70        ),
 71        dtype=np.float32,
 72    )
 73
 74    for i in range(number_of_timepoints):
 75        _eSRRF3D = Workflow(
 76            (
 77                eSRRF3D_ST(verbose=False),
 78                (
 79                    img[
 80                        frames_per_timepoint
 81                        * i : frames_per_timepoint
 82                        * (i + 1)
 83                    ],
 84                ),
 85                {
 86                    "magnification_xy": magnification_xy,
 87                    "magnification_z": magnification_z,
 88                    "radius": radius,
 89                    "PSF_ratio": PSF_ratio,
 90                    "voxel_ratio": voxel_ratio,
 91                    "sensitivity": sensitivity,
 92                    "mode": mode,
 93                    "doIntensityWeighting": doIntensityWeighting,
 94                },
 95            )
 96        )
 97        if macro_pixel_correction:
 98            output_array[i] = macro_pixel_corrector(
 99                _eSRRF3D.calculate(_force_run_type=_force_run_type)[0],
100                magnification=magnification_xy,
101            )
102        else:
103            output_array[i] = _eSRRF3D.calculate(
104                _force_run_type=_force_run_type
105            )[0]
106
107    return output_array.astype(np.float32)