semvec/pypi-packages huggingface dataset:
fret-analysis 3.2.0
Tools for analyzing single-molecule FRET data
Requires Python: >=3.10

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SPDX-FileCopyrightText: 2020 Lukas Schrangl <lukas.schrangl@tuwien.ac.at>

SPDX-License-Identifier: CC-BY-4.0
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# Single-molecule FRET analysis

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4604566.svg)](https://doi.org/10.5281/zenodo.4604566)

This repository contains a Python package and several Jupyter notebooks to analyze single molecule FRET data.
The procedure is discribed in our article published in the [Journal of Visualized Experiments](https://doi.org/10.3791/63124).
The [supplemental information](https://www.jove.com/files/ftp_upload/63124/si.pdf) contains screen shots with step-by-step instructions.
Note that over time, some function names may have changed slightly, but with the current notebooks from this repository it should still be easily possible to follow the manual.

If you use this software in a project resulting in a scientific publication, please [cite](https://doi.org/10.5281/zenodo.4604566) the software.


## Suggested workflow:

- Install the [uv](https://docs.astral.sh/uv/) Python package manager.
  Linux users can use their distribution's package management system to install `uv`.
- Create a new folder.
- Download [Jupyter notebooks for the analysis](https://github.com/schuetzgroup/fret-analysis/tree/master/notebooks) into the folder.
- Navigate into the folder it using a command line prompt.
- Initialize `uv` in this folder by executing

  ```
  uv init --bare
  ```

- Install the FRET analysis python package, either from PyPI,

  ```
  uv add fret-analysis
  ```

  or from Github,

  ```
  uv add git+https://github.com/schuetzgroup/fret-analysis.git
  ```

- Start Jupyter Lab,
  ```
  uv run --with jupyter jupyter lab
  ```

- Open `01. Tracking` notebook. Run each cell and adjust file paths and parameters as needed.
- When finished, do the same using the `02. Filter` notebook.
- Summary plots can be created with the `03. Plots` notebook.
xstatix 1.2.1
Space and Time Algorithm for Transients In X-rays
Requires Python: >=3.10

# STATiX (Space and Time Algorithm for Transients in X-rays)

The Space and Time Algorithm for Transients in X-rays (STATiX) builds upon 
tools from the image and signal processing fields and in particular the 
Multi-Scale Variance Stabilisation Transform 
(MSVST; [Zhang et al. 2008](http://dx.doi.org/10.1109/TIP.2008.924386), 
[Starck et al. 2009](http://dx.doi.org/10.1051/0004-6361/200811388)) 
to provide a complete detection analysis pipeline optimised for finding 
transient sources on X-ray imaging observations. Unlike standard source 
detection codes, STATiX operates on 3-dimensional data cubes with 2-spatial 
and one temporal dimensions. It is therefore sensitive to short and faint 
X-ray flares that may be hidden in the background once the data cube is 
collapsed in time to produce 2-dimensional images. Although the algorithm 
is motivated by transient source searches, it also provides a competitive tool 
for the detection of the general, typically less variable, X-ray source
population present in X-ray observations. See [Ruiz et al. 2024](https://doi.org/10.1093/mnras/stad3339)
for a detailed explanation of the algorithm.

STATiX is distributed as a Python package. The current implementation only 
allows the processing of data for the XMM-Newton EPIC cameras. Upgrading the 
code for other X-ray imaging missions is possible, but beyond our current 
capabilities.


Installation
------------

STATix needs the following software and libraries for a correct installation:
- C/C++ compiler and make
- [CMake](http://www.cmake.org)
- [CFITSIO](https://heasarc.gsfc.nasa.gov/fitsio/) (>V3.31)
- [pkg-config](https://www.freedesktop.org/wiki/Software/pkg-config/)

In Ubuntu (and other Debian-based Linux distributions) these dependencies can be installed via ``apt``:

    sudo apt install gcc make cmake libcfitsio* pkg-config


Once these prerequisites are installed, STATiX can be easily installed using ``pip``:

    pip install xstatix

Although the STATiX source detection pipeline does not need any additional software, some of its side functions related with XMM-Newton data manipulation need a working installation of [SAS](https://www.cosmos.esa.int/web/xmm-newton/what-is-sas). If all the initial data products are already available (images, data cubes, exposure maps, etc) SAS is not needed. Otherwise these products will be generated during running time if SAS is available. All SAS-related functions are in the [xmmsas](https://github.com/ruizca/statix/blob/main/src/statix/xmmsas.py) module.


Examples
---------

We provide [Jupyter notebooks and scripts](https://github.com/ruizca/statix/blob/main/docs/) with examples on how to use STATiX
with XMM-Newton data. 


[![ahead2020](ahead2020_logo.png)](http://ahead.astro.noa.gr/)

[![astropy](https://img.shields.io/badge/powered%20by-AstroPy-orange.svg?style=flat)](http://www.astropy.org/)