Metadata-Version: 2.1
Name: big-fish
Version: 0.4.0
Summary: Toolbox for the analysis of smFISH images.
Home-page: https://github.com/fish-quant/big-fish
Author: Arthur Imbert
Author-email: arthur.imbert.pro@gmail.com
License: BSD 3-Clause License
Description: # Big-FISH
        
        [![Build Status](https://travis-ci.com/fish-quant/big-fish.svg?branch=master)](https://travis-ci.com/fish-quant/big-fish)
        [![codecov](https://codecov.io/gh/fish-quant/big-fish/branch/master/graph/badge.svg)](https://codecov.io/gh/fish-quant/big-fish)
        [![License](https://img.shields.io/badge/license-BSD%203--Clause-green)](https://github.com/fish-quant/big-fish/blob/master/LICENSE)
        [![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)
        
        **Big-FISH** is a python package for the analysis of smFISH images. It includes various methods to **analyze microscopy images**, such **spot detection** and **segmentation of cells and nuclei**. The package allows the user represent the extract properties of a cell as coordinates (see figure below). The ultimate goal is to simplify **large scale statistical analysis** and quantification.
        
        | Cell image (smFISH channel) and its coordinates representation |
        | ------------- |
        | ![](images/plot_cell.png "Nucleus in blue, mRNAs in red, foci in orange and transcription sites in green") |
        
        ## Installation
        
        ### Dependencies
        
        Big-FISH requires Python 3.6 or newer. Additionally, it has the following dependencies:
        
        - numpy (== 1.16.0)
        - scipy (== 1.4.1)
        - scikit-learn (== 0.20.2)
        - scikit-image (== 0.14.2)
        - matplotlib (== 3.0.2)
        - pandas (== 0.24.0)
        - mrc (== 0.1.5)
        
        Updated dependencies might break.
        
        ### Virtual environment
        
        To avoid dependency conflicts, we recommend the the use of a dedicated [virtual](https://docs.python.org/3.6/library/venv.html) or [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) environment.  In a terminal run the command:
        
        ```bash
        conda create -n bigfish_env python=3.6
        source activate bigfish_env
        ```
        
        We recommend two options to then install Big-FISH in your virtual environment.
        
        #### Download the package from PyPi
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Big-FISH. In a terminal run the command:
        
        ```bash
        pip install big-fish
        ```
        
        #### Clone package from Github
        
        Clone the project's [Github repository](https://github.com/fish-quant/big-fish) and install it manually with the following commands:
        
        ```bash
        git clone git@github.com:fish-quant/big-fish.git
        cd big-fish
        pip install .
        ```
        
        ## Usage
        
        Big-FISH provides a toolbox for the full analysis pipeline of smFISH images:
        
        - Use `bigfish.stack` subpackage for I/O operations, preprocessing and postprocessing.
        - Use `bigfish.segmentation` subpackage for nucleus and cell segmentation.
        - Use `bigfish.detection` subpackage for mRNAs detection.
        - Use `bigfish.plot` subpackage for plotting routines.
        - Use `bigfish.classification` subpackage for pattern recognition tasks.
        
        Several examples are available as [Jupyter notebooks](https://github.com/fish-quant/big-fish-examples/tree/master/notebooks).
        
        ## Support
        
        If you have any question relative to the repository, please open an [issue](https://github.com/fish-quant/big-fish/issues). You can also contact [Arthur Imbert](mailto:arthur.imbert@mines-paristech.fr) or [Florian Mueller](mailto:muellerf.research@gmail.com).
        
        ## Roadmap (suggestion)
        
        Version 0.5.0:
        - Switch to tensorflow 2.2.0.
        - Integrate a deep learning model for segmentation.
        
        Version 1.0.0:
        - Complete code coverage.
        - Add sphinx documentation.
        
        ## Development
        
        ### Source code
        
        You can access the latest sources with the commands:
        
        ```bash
        git clone git@github.com:fish-quant/big-fish.git
        cd big-fish
        git checkout develop
        ```
        
        ### Contributing
        
        [Pull requests](https://github.com/fish-quant/big-fish/pulls) are welcome. For major changes, please open an [issue](https://github.com/fish-quant/big-fish/issues) first to discuss what you would like to change.
        
        ### Testing
        
        Please make sure to update tests as appropriate if you open a pull request. You can install exacts dependencies and specific version of [pytest](https://docs.pytest.org/en/latest/) by running the following command:
        
        ```bash
        pip install -r requirements_dev.txt
        ```
        
        To perform unitary tests, run : 
        
        ```bash
        pytest bigfish
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: BSD License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: deeplearning
