A library to perform kinetic modeling of fast single particle tracking experiments
Project description
Spot-On (cli)
-------
This repository collects a series of scripts to analyze data from single particle tracking experiments. The code was initially written by Anders Sejr Hansen and translated to Python by Maxime Woringer. A [Matlab version](https://gitlab.com/tjian-darzacq-lab/spot-on-matlab) exists and is maintained by Anders Sejr Hansen.
This repository only includes the commandline analysis pipeline. A graphical user interface (GUI) is available for a more user-friendly analysis, but is not included in this repository. This repository contains the command-line version, that can be used independently from the GUI.
Although the functions and methods can be called directly, we provide a walk-through tutorial as a [Jupyter](http://jupyter.org) notebook.
# Dependencies
- numpy
- scipy
- lmfit
Optional: jupyter
Your package manager may provide precompiled versions of `numpy` and `scipy`. In that case, it might be worth using those libraries, because compilation can take a significant amount of time.
# Installation (from pip)
`pip install fastspt`
# Installation (from this Gitlab) repository
## Install dependencies
`pip install -r requirements.txt`
Alternatively, you can install the dependencies manually by typing:
`pip install numpy scipy lmfit`
Optional : `pip install jupyter`
## Install fastSPT
Simply run (as root): `python setup.py install`
Check that it worked: `python -c "import fastspt"`
# Tutorial
A short tutorial, demonstrating the capabilities of the software, is available as a Jupyter notebook.
See `fastSPT_tutorial.ipynb`. The tutorial is also available online: **address of the page**
**Document here how to open a Jupyter notebook**
# Usage
## Main functions
## Extra functions
The `writers` submodule contains some useful functions:
- `mat_to_csv(in_path, out_path)` converts a .mat file to a CSV. It can easily be scripted.
## Input file format
## Caveats
# References
# License
This program is released under the GNU General Public License version 3 or upper (GPLv3+).
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
# Deploying (developer only)
```
python setup.py sdist
gpg --detach-sign -a dist/fastspt-11.4.tar.gz
twine upload dist/fastspt-11.4.tar.gz dist/fastspt-11.4.tar.gz.asc
```
# Authors
Maxime Woringer, Anders Sejr Hansen
# Bugs/suggestions
Send to bugtracker or to email.
-------
This repository collects a series of scripts to analyze data from single particle tracking experiments. The code was initially written by Anders Sejr Hansen and translated to Python by Maxime Woringer. A [Matlab version](https://gitlab.com/tjian-darzacq-lab/spot-on-matlab) exists and is maintained by Anders Sejr Hansen.
This repository only includes the commandline analysis pipeline. A graphical user interface (GUI) is available for a more user-friendly analysis, but is not included in this repository. This repository contains the command-line version, that can be used independently from the GUI.
Although the functions and methods can be called directly, we provide a walk-through tutorial as a [Jupyter](http://jupyter.org) notebook.
# Dependencies
- numpy
- scipy
- lmfit
Optional: jupyter
Your package manager may provide precompiled versions of `numpy` and `scipy`. In that case, it might be worth using those libraries, because compilation can take a significant amount of time.
# Installation (from pip)
`pip install fastspt`
# Installation (from this Gitlab) repository
## Install dependencies
`pip install -r requirements.txt`
Alternatively, you can install the dependencies manually by typing:
`pip install numpy scipy lmfit`
Optional : `pip install jupyter`
## Install fastSPT
Simply run (as root): `python setup.py install`
Check that it worked: `python -c "import fastspt"`
# Tutorial
A short tutorial, demonstrating the capabilities of the software, is available as a Jupyter notebook.
See `fastSPT_tutorial.ipynb`. The tutorial is also available online: **address of the page**
**Document here how to open a Jupyter notebook**
# Usage
## Main functions
## Extra functions
The `writers` submodule contains some useful functions:
- `mat_to_csv(in_path, out_path)` converts a .mat file to a CSV. It can easily be scripted.
## Input file format
## Caveats
# References
# License
This program is released under the GNU General Public License version 3 or upper (GPLv3+).
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
# Deploying (developer only)
```
python setup.py sdist
gpg --detach-sign -a dist/fastspt-11.4.tar.gz
twine upload dist/fastspt-11.4.tar.gz dist/fastspt-11.4.tar.gz.asc
```
# Authors
Maxime Woringer, Anders Sejr Hansen
# Bugs/suggestions
Send to bugtracker or to email.
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