Point Spread Function calculations for fluorescence microscopy

Psf is a Python library to calculate Point Spread Functions (PSF) for fluorescence microscopy.

The psf library is no longer actively developed.

Author

Christoph Gohlke

License

BSD 3-Clause

Version

2024.4.24

Quickstart

Install the psf package and all dependencies from the Python Package Index:

python -m pip install -U psf[all]

See Examples for using the programming interface.

Source code and support are available on GitHub.

Requirements

This revision was tested with the following requirements and dependencies (other versions may work):

• CPython 3.9.13, 3.10.11, 3.11.9, 3.12.3
• NumPy 1.26.4
• Matplotlib 3.8.4 (optional for plotting)

Revisions

2024.4.24

• Support NumPy 2.

2024.1.6

• Change PSF.TYPES from dict to set (breaking).

2023.4.26

• Use enums.
• Derive Dimensions from UserDict.
• Add type hints.
• Convert to Google style docstrings.
• Drop support for Python 3.8 and numpy < 1.21 (NEP29).

2022.9.26

• Fix setup.py.

2022.9.12

• Remove support for Python 3.7 (NEP 29).
• Update metadata.

2021.6.6

• Remove support for Python 3.6 (NEP 29).

2020.1.1

• Remove support for Python 2.7 and 3.5.
• Update copyright.

2019.10.14

• Support Python 3.8.

2019.4.22

• Fix setup requirements.
• Fix compiler warning.

References

1. Electromagnetic diffraction in optical systems. II. Structure of the image field in an aplanatic system. B Richards and E Wolf. Proc R Soc Lond A, 253 (1274), 358-379, 1959.
2. Focal volume optics and experimental artifacts in confocal fluorescence correlation spectroscopy. S T Hess, W W Webb. Biophys J (83) 2300-17, 2002.
3. Electromagnetic description of image formation in confocal fluorescence microscopy. T D Viser, S H Wiersma. J Opt Soc Am A (11) 599-608, 1994.
4. Photon counting histogram: one-photon excitation. B Huang, T D Perroud, R N Zare. Chem Phys Chem (5), 1523-31, 2004. Supporting information: Calculation of the observation volume profile.
5. Gaussian approximations of fluorescence microscope point-spread function models. B Zhang, J Zerubia, J C Olivo-Marin. Appl. Optics (46) 1819-29, 2007.
6. The SVI-wiki on 3D microscopy, deconvolution, visualization and analysis. https://svi.nl/NyquistRate

Examples

>>> import psf >>> args = dict( ... shape=(32, 32), ... dims=(4, 4), ... ex_wavelen=488, ... em_wavelen=520, ... num_aperture=1.2, ... refr_index=1.333, ... pinhole_radius=0.55, ... pinhole_shape='round' ... ) >>> obsvol = psf.PSF(psf.GAUSSIAN | psf.CONFOCAL, args) >>> obsvol.sigma.ou (2.588..., 1.370...) >>> obsvol = psf.PSF(psf.ISOTROPIC | psf.CONFOCAL,args) >>> print(obsvol, end='') PSF ISOTROPIC|CONFOCAL shape: (32, 32) pixel dimensions: (4.00, 4.00) um, (55.64, 61.80) ou, (8.06, 8.06) au excitation wavelength: 488.0 nm emission wavelength: 520.0 nm numeric aperture: 1.20 refractive index: 1.33 half cone angle: 64.19 deg magnification: 1.00 underfilling: 1.00 pinhole radius: 0.550 um, 8.498 ou, 1.1086 au, 4.40 px computing time: ... ms >>> obsvol[0, :3] array([1. , 0.51071, 0.04397]) >>> # write the image plane to file >>> obsvol.slice(0).tofile('_test_slice.bin') >>> # write a full 3D PSF volume to file >>> obsvol.volume().tofile('_test_volume.bin')

Refer to psf_example.py in the source distribution for more examples.