Fluorescence#

class deeptrack.optics.Fluorescence(NA: float | Callable[[...], float] = 0.7, wavelength: float | Callable[[...], float] = 6.6e-07, magnification: float | Callable[[...], float] = 10, resolution: float | Tuple[float, ...] | List[float] | ndarray | Callable[[...], float | Tuple[float, ...] | List[float] | ndarray] = 1e-06, refractive_index_medium: float | Callable[[...], float] = 1.33, padding: Tuple[int, ...] | List[int] | ndarray | Callable[[...], Tuple[int, ...] | List[int] | ndarray] = (10, 10, 10, 10), output_region: Tuple[int, ...] | List[int] | ndarray | Callable[[...], Tuple[int, ...] | List[int] | ndarray] = (0, 0, 128, 128), pupil: Feature | None = None, illumination: Feature | None = None, upscale: int = 1, **kwargs: Dict[str, Any])#

Bases: Optics

Optical device for fluorescent imaging.

The Fluorescence class simulates the imaging process in fluorescence microscopy by creating a discretized volume where each pixel represents the intensity of light emitted by fluorophores in the sample. It extends the Optics class to include fluorescence-specific functionalities.

Parameters#

NA: float

Numerical aperture of the optical system.

wavelength: float

Emission wavelength of the fluorescent light (in meters).

magnification: float

Magnification of the optical system.

resolution: array_like[float (, float, float)]

Pixel spacing in the camera. Optionally includes the z-direction.

refractive_index_medium: float

Refractive index of the imaging medium.

padding: array_like[int, int, int, int]

Padding applied to the sample volume to reduce edge effects.

output_region: array_like[int, int, int, int], optional

Region of the output image to extract (x, y, width, height). If None, returns the full image.

pupil: Feature, optional

A feature set defining the pupil function at focus. The input is the unaberrated pupil.

illumination: Feature, optional

A feature set defining the illumination source.

upscale: int, optional

Scaling factor for the resolution of the optical system.

**kwargs: Dict[str, Any]

Attributes#

__gpu_compatible__: bool

Indicates whether the class supports GPU acceleration.

NA: float

Numerical aperture of the optical system.

wavelength: float

Emission wavelength of the fluorescent light (in meters).

magnification: float

Magnification of the optical system.

resolution: array_like[float (, float, float)]

Pixel spacing in the camera. Optionally includes the z-direction.

refractive_index_medium: float

Refractive index of the imaging medium.

padding: array_like[int, int, int, int]

Padding applied to the sample volume to reduce edge effects.

output_region: array_like[int, int, int, int]

Region of the output image to extract (x, y, width, height).

voxel_size: function

Function returning the voxel size of the optical system.

pixel_size: function

Function returning the pixel size of the optical system.

upscale: int

Scaling factor for the resolution of the optical system.

limits: array_like[int, int]

Limits of the volume to be imaged.

fields: list[Feature]

List of fields to be imaged

Methods#

get(illuminated_volume: array_like[complex], limits: array_like[int, int], **kwargs: Dict[str, Any]) -> Image

Simulates the imaging process using a fluorescence microscope.

Examples#

Create a Fluorescence instance:

>>> import deeptrack as dt

>>> optics = dt.Fluorescence(
...     NA=1.4, wavelength=0.52e-6, magnification=60,
... )
>>> print(optics.NA())
1.4

Methods Summary

get(illuminated_volume, limits, **kwargs)

Simulates the imaging process using a fluorescence microscope.

Methods Documentation

get(illuminated_volume: Tuple[complex, ...] | List[complex] | ndarray, limits: Tuple[int, ...] | List[int] | ndarray, **kwargs: Dict[str, Any]) → Image#

Simulates the imaging process using a fluorescence microscope.

This method convolves the 3D illuminated volume with a pupil function to generate a 2D image projection.

Parameters#

illuminated_volume: array_like[complex]

The illuminated 3D volume to be imaged.

limits: array_like[int, int]

Boundaries of the illuminated volume in each dimension.

**kwargs: Dict[str, Any]

Additional properties for the imaging process, such as: - ‘padding’: Padding to apply to the sample. - ‘output_region’: Specific region to extract from the image.

Returns#

Image: Image

A 2D image object representing the fluorescence projection.

Notes#

  • Empty slices in the volume are skipped for performance optimization.

  • The pupil function incorporates defocus effects based on z-slice.

Examples#

Simulate imaging a volume:

>>> import deeptrack as dt
>>> import numpy as np

>>> optics = dt.Fluorescence(
...     NA=1.4, wavelength=0.52e-6, magnification=60,
... )
>>> volume = dt.Image(np.ones((128, 128, 10), dtype=complex))
>>> limits = np.array([[0, 128], [0, 128], [0, 10]])
>>> properties = optics.properties()
>>> filtered_properties = {
...     k: v for k, v in properties.items() 
...     if k in {"padding", "output_region", "NA", 
...              "wavelength", "refractive_index_medium"}
... }
>>> image = optics.get(volume, limits, **filtered_properties)
>>> print(image.shape)
(128, 128, 1)