motion_correction.desktop package

Submodules

motion_correction.desktop.flim_aligner module

class motion_correction.desktop.flim_aligner.FlimAligner

Bases: object

A class for aligning and correcting FLIM (Fluorescence Lifetime Imaging Microscopy) data.

Attributes:

ptfile (str): The path to the input PT3/PTU file. transforms (numpy.ndarray): Transformation matrices estimated from intensity frame alignment. global_method (AlignMethod): The global alignment method (e.g., AlignMethod.PHASE). local_method (AlignMethod): The local alignment method (e.g., AlignMethod.OPTICAL_POLY). channel (int): The channel index selected for intensity frame alignment. shape (tuple): The shape of the FLIM stack data. flim_dict (dict): A dictionary containing the indices and values of non-zero FLIM data entries. flim_frames (numpy.ndarray): FLIM intensity frames before correction. flim_frames_corrected (numpy.ndarray): Corrected FLIM intensity frames. curve_fit (numpy.ndarray): Curve decay curve histogramed data. curve_fit_corrected (numpy.ndarray): Corrected decay curve histogramed data. curve_fit_corrected_int (numpy.ndarray): Corrected curve histogramed data as integers. save_dir (str): The directory where results will be saved. sim_metric (SimMetric): The similarity metric for frame alignment (e.g., SimMetric.NCC). old_sim (numpy.ndarray): Similarity values before alignment for all intensity frames. new_sim (numpy.ndarray): Similarity values after alignment for all intensity frames. meta (dict): Metadata associated with the FLIM data.

Methods:

set_methods(global_method=None, local_method=None): Set global and local alignment methods. set_sim_metric(sim=SimMetric.NCC): Set the similarity metric for alignment. get_intensity_stack(pt_file, is_raw=False): Load and prepare FLIM intensity data. apply_correction_intensity(ptfile, ref_frame_idx=0): Apply intensity correction to FLIM frames. export_results(save_dir=None): Export corrected results to files. apply_correction_flim(): Apply FLIM data correction.

apply_correction_flim()

Apply FLIM data correction to the loaded data.

This method applies FLIM data correction to the loaded raw FLIM data. It uses the transformations estimated in function apply_correction_intensity to correct the data.

Note:

This method assumes that the FLIM data has already been loaded using the get_intensity_stack method.

apply_correction_intensity(ptfile, ref_frame_idx=0)

Apply intensity correction to FLIM frames.

Args:

ptfile (str): The path to the PT3 file. ref_frame_idx (int): The index of the reference frame for alignment.

export_results(save_dir=None)

Export corrected results to files.

Args:

save_dir (str): The directory where results will be saved.

get_intensity_stack(pt_file, is_raw=False)

Load and prepare FLIM intensity data from a PT3 file.

Args:

pt_file (str): The path to the PT3 file. is_raw (bool): Whether the data is raw intensity data.

set_channel(channel: int)

set_methods(global_method: _CorrectionAlgorithm | None = None, local_method: _CorrectionAlgorithm | None = None)

Set the global and local alignment methods.

Args:

global_method (AlignMethod): The global alignment method. local_method (AlignMethod): The local alignment method.

set_sim_metric(sim=SimMetric.NCC)

Set the similarity metric for frame alignment.

Args:

sim (SimMetric): The similarity metric to use.

class motion_correction.desktop.flim_aligner.SimMetric(value)

Bases: Enum

An enumeration.

MSE = 'mse'

NCC = 'ncc'

NRM = 'nrm'

SPM = 'spm'

SSI = 'ssi'

motion_correction.desktop.utility module

motion_correction.desktop.utility.display_images(images: List[str | ndarray], rows: int | None = None, cols: int | None = None, colorbar=True, figsize: Tuple[int, int] = (12, 12)) → None

Display a list of images in a grid layout using Matplotlib.

Args:

images (list): List of image paths or NumPy arrays. rows (int): Number of rows in the grid (default: None, automatic calculation). cols (int): Number of columns in the grid (default: None, automatic calculation). figsize (tuple): Size of the figure (default: (10, 10)).

Note: This function displays a grid of images using Matplotlib. It supports both image paths and NumPy arrays as input.

motion_correction.desktop.utility.join_path(*args)

motion_correction.desktop.utility.ncc(patch1, patch2)

motion_correction.desktop.utility.plot_grids(u: ndarray, v: ndarray, ax: Axes | None = None, **kwargs) → None

Plot grid lines on a Matplotlib Axes object.

Parameters: - u (np.ndarray): Horizontal grid lines. - v (np.ndarray): Vertical grid lines. - ax (plt.Axes, optional): Matplotlib Axes object to plot on (default: None).

Note: This function plots grid lines on a Matplotlib Axes object using horizontal and vertical arrays u and v.

motion_correction.desktop.utility.plot_sequence_images(image_array)

Display a sequence of images as an animation/video in a Jupyter notebook.

Args:

image_array (numpy.ndarray): An array of images with shape (num_images, height, width, num_channels).

Note: This function displays a sequence of images as an animation in a Jupyter notebook.

motion_correction.desktop.utility.save_sequence_images(file_path, image_array, fps=15)

Save a sequence of images as a video file.

Args:

file_path (str): The file path to save the video. image_array (numpy.ndarray): An array of images with shape (num_images, height, width, num_channels). fps (int): Frames per second for the video (default: 15).

Note: This function saves a sequence of images as a video file at the specified file_path.

Module contents