nanopyx.methods.esrrf.eSRRF_workflow

 1from ..workflow import Workflow
 2from ...core.transform import eSRRF_ST
 3import numpy as np
 4
 5# TODO check correlations and error map
 6
 7
 8def eSRRF(
 9    image,
10    magnification: int = 5,
11    radius: float = 1.5,
12    sensitivity: float = 1,
13    doIntensityWeighting: bool = True,
14    _force_run_type=None,
15):
16    """
17    Perform eSRRF analysis on an image.
18
19    Args:
20          image (numpy.ndarray): The input image for eSRRF analysis.
21          magnification (int, optional): Magnification factor (default is 5).
22          radius (float, optional): Radius parameter for eSRRF analysis (default is 1.5).
23          sensitivity (float, optional): Sensitivity parameter for eSRRF analysis (default is 1).
24          doIntensityWeighting (bool, optional): Enable intensity weighting (default is True).
25          _force_run_type (str, optional): Force a specific run type for the analysis (default is None).
26
27    Returns:
28          numpy.ndarray: The result of eSRRF analysis, typically representing the localizations.
29
30    Example:
31          result = eSRRF(image, magnification=5, radius=1.5, sensitivity=1, doIntensityWeighting=True)
32
33    Note:
34          - eSRRF (enhanced Super-Resolution Radial Fluctuations) is a method for super-resolution localization microscopy.
35          - This function sets up a workflow to perform eSRRF analysis on the input image.
36          - The workflow includes eSRRF_ST as a step and can be customized with various parameters.
37          - The result is typically a numpy array representing the localized points.
38
39    See Also:
40          - eSRRF_ST: The eSRRF step that performs the actual analysis.
41          - Workflow: The class used to define and run analysis workflows.
42    """
43
44    _eSRRF = Workflow(
45        (
46            eSRRF_ST(verbose=False),
47            (image,),
48            {
49                "magnification": magnification,
50                "radius": radius,
51                "sensitivity": sensitivity,
52                "doIntensityWeighting": doIntensityWeighting,
53            },
54        )
55    )
56
57    return _eSRRF.calculate(_force_run_type=_force_run_type)