nanopyx.methods.esrrf.parameter_sweep

 1import numpy as np
 2from matplotlib import pyplot as plt
 3from ...core.analysis.parameter_sweep import ParameterSweep
 4
 5
 6def run_esrrf_parameter_sweep(
 7    img: np.ndarray,
 8    magnification: int = 2,
 9    sensitivities: list = [1, 2],
10    radii: list = [1, 1.5],
11    temporal_correlation: str = "AVG",
12    use_decorr: bool = False,
13    plot_sweep=False,
14    return_qnr=False,
15    n_frames=None,
16):
17    """
18    Conducts a parameter sweep for enhanced Super-Resolution Radial Fluctuations (eSRRF) analysis on an image.
19
20    Parameters
21    ----------
22    img : np.ndarray
23        The input image as a 2D numpy array.
24    magnification : int, optional
25        The magnification factor to be applied during the ESRRF analysis. Default is 2.
26    sensitivities : list of int, optional
27        A list of sensitivity values to be used in the parameter sweep. Default is [1, 2].
28    radii : list of float, optional
29        A list of radii values to be used in the parameter sweep. Default is [1, 1.5].
30    temporal_correlation : str, optional
31        The method of temporal correlation to be used. Default is "AVG".
32    return_qnr : bool, optional
33        If True, return an array with QnR value for each combination of sensitivities and radii. 
34        If False, return a tuple of optimal sensitivity and radius values.
35    n_frames : int, optional
36        if None, uses all frames, otherwise splits the data into batches with size = n_frames.
37
38
39    Returns
40    -------
41    np.ndarray or tuple
42        If return_qnr is True, returns an array with QnR value for each combination of sensitivities and radii. Indices of dimension 0 corresponds to indices of sensitivities list and dimension 1 to radii list.
43        Suggestion: sensitivity_index, radius_index = np.argmax(run_esrrf_parameter_sweep())
44        Optimal parameters will then correspond to: sensitivities[sensitivity_index] and radii[radius_index]
45        If return_qnr is False, returns a tuple of optimal sensitivity and radius values.
46
47    Notes
48    -----
49    This function performs a parameter sweep for the ESRRF method, which is used for super-resolution imaging analysis. It varies sensitivity and radius to find optimal settings for image enhancement.
50    """
51    ps = ParameterSweep()
52    out = ps.run(
53        img,
54        magnification,
55        sensitivity_array=sensitivities,
56        radius_array=radii,
57        temporal_correlation=temporal_correlation,
58        use_decorr=use_decorr,
59        n_frames=n_frames
60    )
61
62    if plot_sweep:
63        fig, ax = plt.subplots()
64        ax.imshow(out, cmap="plasma")
65        ax.set_xticks(np.arange(len(radii)), labels=radii)
66        ax.set_yticks(np.arange(len(sensitivities)), labels=sensitivities)
67        print(range(len(sensitivities)), range(len(radii)))
68        for i in range(len(sensitivities)):
69            for j in range(len(radii)):
70                ax.text(j, i, round(out[i, j], 2), ha="center", va="center", color="w")
71        ax.set_title("Parameter Sweep QnR")
72        ax.set_xlabel("Radii")
73        ax.set_ylabel("Sensitivities")
74        fig.tight_layout()
75        plt.show()
76
77    if return_qnr:
78        return out
79    else:
80        sens_idx, rad_idx = np.unravel_index(np.argmax(out), out.shape)
81        return sensitivities[sens_idx], radii[rad_idx]