Scalar and vectorial models of the microscope point spread function (PSF).

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for talley from gravatar.com talley

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: GNU General Public License v3 (GPLv3) (GPL-3.0)
  • Author: Talley Lambert
  • Requires: Python >=3.7
Classifiers

Project description

psfmodels

PyPI Python Version CI codecov

Python bindings for scalar and vectorial models of the point spread function.

Original C++ code and MATLAB MEX bindings Copyright © 2006-2013, Francois Aguet, distributed under GPL-3.0 license. Python bindings by Talley Lambert

The model is described in Auget et al 20091. For more information and implementation details, see Francois' Thesis2.

1 F. Aguet et al., (2009) Opt. Express 17(8), pp. 6829-6848

2 F. Aguet. (2009) Super-Resolution Fluorescence Microscopy Based on Physical Models. Swiss Federal Institute of Technology Lausanne, EPFL Thesis no. 4418

see also:

For a different (faster) scalar-based Gibson–Lanni PSF model, see the MicroscPSF project, based on Li et al (2017) which has been implemented in Python, MATLAB, and ImageJ/Java

Install

Prebuilt binaries available on pypi for OS X and Windows, sdist available for linux

pip install psfmodels

from source

git clone https://github.com/tlambert03/PSFmodels.git
cd PSFmodels
pip install -e ".[dev]"  # will compile c code via pybind11

Usage

There are two main functions in psfmodels: vectorial_psf and scalar_psf. Additionally, each version has a helper function called vectorial_psf_centered and scalar_psf_centered respectively. The main difference is that the _psf functions accept a vector of Z positions zv (relative to coverslip) at which PSF is calculated. As such, the point source may or may not actually be in the center of the rendered volume. The _psf_centered variants, by contrast, do not accecpt zv, but rather accept nz (the number of z planes) and dz (the z step size in microns), and always generates an output volume in which the point source is positioned in the middle of the Z range, with planes equidistant from each other. All functions accept an argument pz, specifying the position of the point source relative to the coverslip. See additional keyword arguments below

Note, all output dimensions (nx and nz) should be odd.

import psfmodels as psfm
import matplotlib.pyplot as plt
from matplotlib.colors import PowerNorm

# generate centered psf with a point source at `pz` microns from coverslip
# shape will be (127, 127, 127)
psf = psfm.make_psf(127, 127, dxy=0.05, dz=0.05, pz=0)
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(psf[nz//2], norm=PowerNorm(gamma=0.4))
ax2.imshow(psf[:, nx//2], norm=PowerNorm(gamma=0.4))
plt.show()

Image of PSF

# instead of nz and dz, you can directly specify a vector of z positions
import numpy as np

# generate 31 evenly spaced Z positions from -3 to 3 microns
psf = psfm.make_psf(np.linspace(-3, 3, 31), nx=127)
psf.shape  # (31, 127, 127)

all PSF functions accept the following parameters. Units should be provided in microns unless otherwise stated. Python API may change slightly in the future. See function docstrings as well.

nx (int):       XY size of output PSF in pixels, must be odd.
dxy (float):    pixel size in sample space (microns) [default: 0.05]
pz (float):     depth of point source relative to coverslip (in microns) [default: 0]
ti0 (float):    working distance of the objective (microns) [default: 150.0]
ni0 (float):    immersion medium refractive index, design value [default: 1.515]
ni (float):     immersion medium refractive index, experimental value [default: 1.515]
tg0 (float):    coverslip thickness, design value (microns) [default: 170.0]
tg (float):     coverslip thickness, experimental value (microns) [default: 170.0]
ng0 (float):    coverslip refractive index, design value [default: 1.515]
ng (float):     coverslip refractive index, experimental value [default: 1.515]
ns (float):     sample refractive index [default: 1.47]
wvl (float):    emission wavelength (microns) [default: 0.6]
NA (float):     numerical aperture [default: 1.4]

Comparison with other models

While these models are definitely slower than the one implemented in Li et al (2017) and MicroscPSF, there are some interesting differences between the scalar and vectorial approximations, particularly with higher NA lenses, non-ideal sample refractive index, and increasing spherical aberration with depth from the coverslip.

For an interactive comparison, see the examples.ipynb Jupyter notebook.

Lightsheet PSF utility function

The psfmodels.tot_psf() function provides a quick way to simulate the total system PSF (excitation x detection) as might be observed on a light sheet microscope (currently, only strictly orthogonal illumination and detection are supported). See the lightsheet.ipynb Jupyter notebook for examples.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for talley from gravatar.com talley

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: GNU General Public License v3 (GPLv3) (GPL-3.0)
  • Author: Talley Lambert
  • Requires: Python >=3.7
Classifiers

Release history Release notifications | RSS feed

0.3.3

0.3.2

0.3.1

This version

0.3.0

0.2.0

0.1.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

psfmodels-0.3.0.tar.gz (575.5 kB view details)

Uploaded Source

Built Distributions

psfmodels-0.3.0-cp310-cp310-win_amd64.whl (106.5 kB view details)

Uploaded CPython 3.10 Windows x86-64

psfmodels-0.3.0-cp310-cp310-musllinux_1_1_x86_64.whl (665.1 kB view details)

Uploaded CPython 3.10 musllinux: musl 1.1+ x86-64

psfmodels-0.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (150.0 kB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

psfmodels-0.3.0-cp310-cp310-macosx_11_0_arm64.whl (101.4 kB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

psfmodels-0.3.0-cp310-cp310-macosx_10_9_x86_64.whl (110.5 kB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

psfmodels-0.3.0-cp39-cp39-win_amd64.whl (106.5 kB view details)

Uploaded CPython 3.9 Windows x86-64

psfmodels-0.3.0-cp39-cp39-musllinux_1_1_x86_64.whl (666.1 kB view details)

Uploaded CPython 3.9 musllinux: musl 1.1+ x86-64

psfmodels-0.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (150.3 kB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

psfmodels-0.3.0-cp39-cp39-macosx_11_0_arm64.whl (101.6 kB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

psfmodels-0.3.0-cp39-cp39-macosx_10_9_x86_64.whl (110.6 kB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

psfmodels-0.3.0-cp38-cp38-win_amd64.whl (106.5 kB view details)

Uploaded CPython 3.8 Windows x86-64

psfmodels-0.3.0-cp38-cp38-musllinux_1_1_x86_64.whl (665.0 kB view details)

Uploaded CPython 3.8 musllinux: musl 1.1+ x86-64

psfmodels-0.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (150.0 kB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

psfmodels-0.3.0-cp38-cp38-macosx_11_0_arm64.whl (101.4 kB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

psfmodels-0.3.0-cp38-cp38-macosx_10_9_x86_64.whl (110.3 kB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

psfmodels-0.3.0-cp37-cp37m-win_amd64.whl (107.2 kB view details)

Uploaded CPython 3.7m Windows x86-64

psfmodels-0.3.0-cp37-cp37m-musllinux_1_1_x86_64.whl (666.9 kB view details)

Uploaded CPython 3.7m musllinux: musl 1.1+ x86-64

psfmodels-0.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (150.4 kB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

psfmodels-0.3.0-cp37-cp37m-macosx_10_9_x86_64.whl (109.9 kB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file psfmodels-0.3.0.tar.gz.

File metadata

  • Download URL: psfmodels-0.3.0.tar.gz
  • Upload date:
  • Size: 575.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for psfmodels-0.3.0.tar.gz
Algorithm Hash digest
SHA256 9d5baee8cc99ad4a8399d272c2a4b13f8a7ba49f54302f6d1746467d69a89e42
MD5 eafc64e6b47118462c8e787ce16f8033
BLAKE2b-256 4309bf7f316a52b53963ffd9c204621f73c7806ae28ecacdda7329f4adf77967

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 d8b3e364c2a1c0244c10d5f833b8ae77ebaa214c25fa99dbf50439d26eeb4253
MD5 d2a404c7cb609dca5b8e171e7688250a
BLAKE2b-256 1d0999629f63dc68bd8ec6ece4fd551cae96b2a8a5e5bb32be65804edb38cb1c

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp310-cp310-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp310-cp310-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 4d1427ccde696630827e4fe052f3d72a9083b37b9ae4476bb6a4d693361980ed
MD5 59003b40a0df21d73e1c42f5bdd19539
BLAKE2b-256 4f37949891de3631307c32e501434e75bf34b2da415fc5064f1f997dac32afc5

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 2ef48c66bcb07bb26c401c7a52669f0f4fab8dd41fdd118087f5dc9d02af58c8
MD5 2e0beab39ce531ce3db5e601766f5453
BLAKE2b-256 3c8c13fb83a8512d4fd7b7b1aced492e58779980257ca9491c16f2fa1f2ca539

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 8ad92844a2a3ebc380be1c13b72f70e60c930b4a01cc8f98040dcf2a7255712c
MD5 0ff7611661ca347c95c32e139047fc7e
BLAKE2b-256 6cf976c627252ccd565dabafaf16f8be64559f7a6013ee47450e0de06ea898ca

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 dfd292f32844b34f1ad13ca9f61a834571b2475b71cd81c61773a2ee98bcc3c0
MD5 d9f5398adac9035d0da3d7960eb28cac
BLAKE2b-256 414422bd33401b9af2c70434b990655a011a42f24502c3cf45b7224c8b387ae2

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: psfmodels-0.3.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 106.5 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for psfmodels-0.3.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 6ec04a5a8a30f9255850b6a9604c1613873259704831cc75b0510390f49a229b
MD5 a7574bda71ec53dd18b6dd18d11aa160
BLAKE2b-256 aca49402720f623cd8267b99e83df9e6a0ccd0a5c801cb71789ff86356b184d0

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp39-cp39-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp39-cp39-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 1671700ebe598635bb36e4b0db7846f2bb955c153ddf295cdf8ece15f654ea7e
MD5 a492f470feb4a3a2d83ae8db14fc1a90
BLAKE2b-256 e7e8b7b90541d6f16939d4415fd0f88d235dc64ebcfab9d4dbefc3f15bae0f3c

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a3d0581841505fa9e3888b335c4acfc8fe9198015f8f71378c5879cf733d4ed9
MD5 76f320bd579a79fd62d417c25b6ae4eb
BLAKE2b-256 2cac2bad4b09a2b4f84188f8f3b312966920f554b499878ec825eeb6ccd70e44

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 fcf8e922f862a9a03d33d3d75474039106b28fce026451711717e440f9c4a5f5
MD5 7eaa306b88a7f1d35fafe08802fefcae
BLAKE2b-256 f5e995ba9cce01667d7f9a6ef4dffabef4c03c9ddb465c6ea6596aa74d589b36

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 0400adb25aebbb2afbfab02cafe828cb81493d8f532b0dc9aaca317b2ac31203
MD5 55f831fbce15f6fe8de438b47ab0dde4
BLAKE2b-256 80c13b46b1164ba0b5af613696778a94a6cdfee4886b7cd91df180b0b15872e6

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: psfmodels-0.3.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 106.5 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for psfmodels-0.3.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 3a384678c53af9e187533e7179e0dfe9c8b295feff05d5250d6fd1e2cdac052d
MD5 3326946357fddd06a0c092fb4e76037b
BLAKE2b-256 7ace108cbad15b80dc3cc73d39ac59d639515c06f5c9893375d5eaffea25a40e

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp38-cp38-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp38-cp38-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 94e3ea46e962a0b67a91183ae5409779e1bc793e8430f15efd72471f338023da
MD5 164ed472b5d2f05d92b46de2480b8aa3
BLAKE2b-256 407ced5e12064a604b0838f04855876fee97b0db3fad241b5942c36a8249a925

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 cb19cc00adb5624c6c79467c63ecebfbd4f2d4d8b68365406b6f4a48ee6883e5
MD5 2f8d2070c255446618affce8fe8acce5
BLAKE2b-256 be433871f2b428b7a0c7c8c52c77e1ef609572760c0722fc290c6ff4bd315fc7

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 935c5abf8ae0d171359336ec7c751efe745d825e6dfd9c319755c629a506ea75
MD5 fc4f87969e469793819b37b30b8ee538
BLAKE2b-256 b1217f2988c12b20aea4729d71aae97229dddfaadde132978cdb41608c8942f9

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 af174880771cd4fb3d0a7d7277f4e87bf6134336f2c7e5cc01f5f0ef9db74207
MD5 5e7bde438f67fa7a94a46b9e3b93b72d
BLAKE2b-256 5cc784a96214c533b943ed7fda680bf1625da6856ecc57e6ca7456b19d2c5788

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: psfmodels-0.3.0-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 107.2 kB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for psfmodels-0.3.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 4ca923c7c7dc31358a00c987c383d917ac478487e77c33312717a40df4792b55
MD5 87b59bd1489e52521e35a43afb9ffb61
BLAKE2b-256 1583e293ed88f3ffa656e4a23417f2fec480e5ea8e8d034e937d579c5083554c

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp37-cp37m-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp37-cp37m-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 88b081e13ae9e99f8889c39a205dbd6e536d5f0a2b309d7a5f9cc2fd21b58990
MD5 b14a4907df99b2686052cd4d0227ab15
BLAKE2b-256 d26980a900aa96912f8c9341ea26bb9ca29d932e2f38444354160700cd059a9e

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 341bdceba0ab132e60ce1f436f2c99d9dfb5d82046bd26b7025ed1c0687f10ac
MD5 5cb77a9e7b6fcfe593ff11e930c82012
BLAKE2b-256 5422866cdc30fa57c3600d5ff88d77601fbeed33fb179fcebb8b6d0d629f8c05

See more details on using hashes here.

File details

Details for the file psfmodels-0.3.0-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for psfmodels-0.3.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 7743c41106d346012365b87d5360a7520522d0dd8784f66f8a879c7b7547310d
MD5 4cf6fedbc61b917bb60582c825aaa577
BLAKE2b-256 cc2c9a43eddc0b1c19af892418a521b444e39878be1fbdf5af3c24ee838f2198

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page