picassosr 0.7.0

Description:

picassosr 0.7.0

A collection of tools for painting super-resolution images. The Picasso software is complemented by our Nature Protocols publication.
A comprehensive documentation can be found here: Read the Docs.

Picasso 0.7.0
Adaptive Intersection Maximization (AIM, doi: 10.1126/sciadv.adm7765) implemented in Picasso.


Previous versions
To see all changes introduced in previous versions, click here.


Installation
Check out the Picasso release page to download and run the latest compiled one-click installer for Windows. Here you will also find the Nature Protocols legacy version.
For the platform-independent usage of Picasso (e.g., with Linux and Mac Os X), please follow the advanced installation instructions below.

Other installation modes (Python 3.10)
As an alternative to the stand-alone program for end-users, Picasso can be installed as a Python package. This is the preferred option to use Picasso’s internal routines in custom Python programs. Those can be imported by running, for example, from picasso import io (see the “Example usage” tab below) to use input/output functions from Picasso. For windows, it is still possible to use Picasso as an end-user by creating the respective shortcuts. This allows Picasso to be used on the same system by both programmers and end-users.

Via PyPI

Open the console/terminal and create a new conda environment: conda create --name picasso python=3.10
Activate the environment: conda activate picasso.
Install Picasso package using: pip install picassosr.
You can now run any Picasso function directly from the console/terminal by running: picasso render, picasso localize, etc, or import Picasso functions in your own Python scripts.



For Developers
If you wish to use your local version of Picasso with your own modifications:

Open the console/terminal and create a new conda environment: conda create --name picasso python=3.10
Activate the environment: conda activate picasso.
Change to the directory of choice using cd.
Clone this GitHub repository by running git clone https://github.com/jungmannlab/picasso. Alternatively, download the zip file and unzip it.
Open the Picasso directory: cd picasso.
You can modify Picasso code in this directory.
To create a local Picasso package to use it in other Python scripts, run pip install -e .. When you change the code in the picasso directory, the changes will be reflected in the package.
You can now run any Picasso function directly from the console/terminal by running: picasso render, picasso localize, etc, or import Picasso functions in your own Python scripts.



Optional packages
Regardless of whether Picasso was installed via PyPI or by cloning the GitHub repository, some packages may be additionally installed to allow extra functionality:

pip install pyinstaller if you plan to additionally compile your own installer with Pyinstaller.
(Windows only) pip install PyImarisWriter==0.7.0 to enable .ims files in Localize and Render. Note that PyImarisWriter has been tested only on Windows.
To enable GPU fitting, follow instructions on Gpufit to install the Gpufit python library in your conda environment. In practice, this means downloading the zipfile and installing the Python wheel. Picasso Localize will automatically import the library if present and enables a checkbox for GPU fitting when selecting the LQ-Method.



Updating
If Picasso was installed from PyPI, run the following command:
pip install --upgrade picassosr



Creating shortcuts on Windows (optional)
Run the PowerShell script “createShortcuts.ps1” in the gui directory. This should be doable by right-clicking on the script and choosing “Run with PowerShell”. Alternatively, run the command
powershell ./createShortcuts.ps1 in the command line. Use the generated shortcuts in the top level directory to start GUI components. Users can drag these shortcuts to their Desktop, Start Menu or Task Bar.



Example Usage
Besides using the GUI, you can use picasso like any other Python module. Consider the following example::
from picasso import io, postprocess

path = 'testdata_locs.hdf5'
locs, info = io.load_locs(path)
# Link localizations and calcualte dark times
linked_locs = postprocess.link(picked_locs, info, r_max=0.05, max_dark_time=1)
linked_locs_dark = postprocess.compute_dark_times(linked_locs)

print('Average bright time {:.2f} frames'.format(np.mean(linked_locs_dark.n)))
print('Average dark time {:.2f} frames'.format(np.mean(linked_locs_dark.dark)))
This codeblock loads data from testdata_locs and uses the postprocess functions programmatically.


Jupyter Notebooks
Check picasso/samples/ for Jupyter Notebooks that show how to interact with the Picasso codebase.


Contributing
If you have a feature request or a bug report, please post it as an issue on the GitHub issue tracker. If you want to contribute, put a PR for it. You can find more guidelines for contributing here. We will gladly guide you through the codebase and credit you accordingly. Additionally, you can check out the Projects-page on GitHub. You can also contact us via [email protected].


Contributions & Copyright

Contributors: Joerg Schnitzbauer, Maximilian Strauss, Rafal Kowalewski, Adrian Przybylski, Andrey Aristov, Hiroshi Sasaki, Alexander Auer, Johanna Rahm
Copyright (c) 2015-2019 Jungmann Lab, Max Planck Institute of Biochemistry
Copyright (c) 2020-2021 Maximilian Strauss
Copyright (c) 2022-2024 Rafal Kowalewski



Citing Picasso
If you use picasso in your research, please cite our Nature Protocols publication describing the software.

J. Schnitzbauer*, M.T. Strauss*, T. Schlichthaerle, F. Schueder, R. Jungmann
Super-Resolution Microscopy with DNA-PAINT
Nature Protocols (2017). 12: 1198-1228 DOI: https://doi.org/10.1038/nprot.2017.024

If you use some of the functionalities provided by Picasso, please also cite the respective publications:


Nearest Neighbor based Analysis (NeNA) for experimental localization precision. DOI: https://doi.org/10.1007/s00418-014-1192-3
Theoretical localization precision (Gauss LQ and MLE). DOI: https://doi.org/10.1038/nmeth.1447
MLE fitting. DOI: https://doi.org/10.1038/nmeth.1449
AIM undrifting. DOI: 10.1126/sciadv.adm776



Credits

Design icon based on “Hexagon by Creative Stalls 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 NounProject.com”