This release is a pre-release and may not be stable for production use.
Picasso
A collection of tools for painting super-resolution images, covering single-molecule localization microscopy (SMLM) analysis from raw movies to localization, rendering and quantification. Picasso is complemented by our Nature Protocols publication.
Documentation: picassosr.readthedocs.io - Installation, First steps, Python API, Changelog.
Installation
One-click installer (Windows, macOS): download it from the release page.
PyPI: pip install picassosr, then start a module with picasso render, picasso localize, etc.
Optional extras (file formats, GPU support) and the developer installation are described in the installation guide.
Example Usage
Besides the GUI, Picasso can be used like any other Python package:
from picasso import io, postprocess
locs, info = io.load_locs("testdata_locs.hdf5")
# Link localizations and calculate dark times
linked_locs, linked_info = postprocess.link(
locs, info, r_max=10, max_dark_time=1
)
linked_locs, linked_info = postprocess.compute_dark_times(
linked_locs, linked_info
)
print(f"Average bright time {linked_locs['len'].mean():.2f} frames")
print(f"Average dark time {linked_locs['dark'].mean():.2f} frames")
For more examples, see the sample notebooks and the Python API documentation.
Contributing
Please post feature requests and bug reports on the issue tracker; pull requests are welcome (see the contributing guidelines). You can also contact us via picasso@jungmannlab.org.
Contributions & Copyright
Citing Picasso
If you use Picasso in your research, please cite our Nature Protocols publication describing the software.
Many of the functionalities provided by Picasso were published independently, please also cite the respective publications:
Feature |
Citation |
DOI |
|---|---|---|
Fitting. All methods are ports of Gpufit, see the Gpufit license |
Przybylski et al., Sci. Rep., 2017 |
|
Experimental PSF (cubic-spline) fitting. The calibration follows the coefficient scheme of Gpuspline, see the Gpuspline license |
Li et al., Nat. Methods, 2018; Babcock & Zhuang, Sci. Rep., 2017 |
|
Multichannel (global) experimental-PSF fitting (globLoc) |
Li et al., Nat. Commun., 2022 |
|
3D fitting via astigmatism |
Huang et al., Science, 2008 |
|
B-spline wavelet spot identification |
Izeddin et al., Opt. Express, 2012 |
|
sCMOS pixel-dependent noise modeling |
Huang et al., Nat. Methods, 2013 |
|
NeNA |
Endesfelder et al., Histochem. Cell Biol., 2014 |
|
FRC |
Nieuwenhuizen et al., Nat. Methods, 2013 |
|
Theoretical lateral localization precision (lpx / lpy, Gaussian least-squares) |
Mortensen et al., Nat. Methods, 2010 |
|
Theoretical axial localization precision (lpz, Gaussian) |
Kowalewski et al., Nat. Commun., 2026 |
|
Quad-tree adaptive histogram rendering |
Baddeley et al., Microsc. Microanal., 2010 |
|
RCC undrifting |
Wang et al., Opt. Express, 2014 |
|
AIM undrifting. Adapted from the authors’ code, see the AIM license |
Ma et al., Sci. Adv., 2024 |
|
SMLM clusterer |
Schlichthaerle et al., Nat. Commun., 2021; Reinhardt et al., Nature, 2023 |
|
DBSCAN |
Ester et al., KDD, 1996 (Vol. 96, No. 34, pp. 226-231) |
|
Anisotropic DBSCAN (inspired by) |
Lörzing et al., J. Phys. Chem. B, 2024 |
|
HDBSCAN |
Campello et al., PAKDD, 2013 |
|
RESI |
Reinhardt et al., Nature, 2023 |
|
Nanotron |
Auer et al., Bioinformatics, 2020 |
|
Picasso Server |
Strauss, Commun. Biol., 2022 |
|
SPINNA |
Masullo et al., Nat. Commun., 2025 |
|
SPINNA for labeling efficiency (LE) fitting |
Hellmeier et al., Nat. Methods, 2024 |
|
G5M |
Kowalewski et al., Nat. Commun., 2026 |
|
Coordinate-based colocalization (CBC) |
Malkusch et al., Histochem. Cell Biol., 2012 |
|
Ripley’s K and H functions |
Ripley, J. R. Stat. Soc. B, 1977 |
|
Two-channel (cross) Ripley’s H and radial distribution functions |
Lotwick and Silverman, J. R. Stat. Soc. B, 1982 |
|
Stitching tiles (global tile positions, inspired by) |
Preibisch et al., Bioinformatics, 2009 |
|
Single-particle tracking (nearest-neighbor tracking, jump distance and MSD analysis) |
Martens et al., Front. Bioinform., 2021 |
Credits
Design icon based on “Hexagon” by Creative Stall from the Noun Project
Simulate icon based on “Microchip” by Futishia from the Noun Project
Localize icon based on “Mountains” by MONTANA RUCOBO from the Noun Project
Filter icon based on “Funnel” by José Campos from the Noun Project
Render icon based on “Paint Palette” by Vectors Market from the Noun Project
Average icon based on “Layers” by Creative Stall from the Noun Project
Server icon based on “Database” by Nimal Raj from the Noun Project
SPINNA icon based on “Spinner” by Viktor Ostrovsky from the Noun Project
The Noun Project icons above are used under the CC BY 3.0 license.
Toolbar and menu icons from Lucide (ISC license). License can be found here.
2 maintainers
Metadata
2 maintainers
Release files for picassosr 1.0.0a1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| picassosr-1.0.0a1.tar.gz | 2.6 MB | With provenance | Details for picassosr-1.0.0a1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| picassosr-1.0.0a1-py3-none-any.whl | Python 3 | none | any | Details for picassosr-1.0.0a1-py3-none-any.whl |
Total release size: 4.5 MB
Release files / picassosr-1.0.0a1.tar.gz
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