Metadata-Version: 2.1
Name: croparray
Version: 0.1.0
Summary: Python module for for creating and manipulating an array of crops (or regions of interest) from images obtained using single-molecule microscopy.
Home-page: https://github.com/Colorado-State-University-Stasevich-Lab/croparray
Author: Tim Stasevich
Author-email: Tim.Stasevich@colostate.edu
License: BSD 3-Clause License
Description: # croparray
        Authors: Tim Stasevich and Luis Aguilera.
        
        ## Description
        This module is intended for creating and manipulating an array of crops (or regions of interest) that were generated from a multicolor TIF video obtained from single-molecule microscopy.
        
        <img src= https://github.com/Colorado-State-University-Stasevich-Lab/croparray/raw/main/docs/images/Fig1-CropArrayConceptV4.png alt="drawing" width="600"/>
        
        
        ## Documentation 
        * Documentation is accessible via [croparray.readthedocs](https://croparray.readthedocs.io) 
        
        ## Colab implementation
        
         * Implementation in Google Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)]( https://colab.research.google.com/drive/1Ru-_ak9PpW9bGM_H9SlffmdDQOxG16-4?usp=sharing)
        
        <img src= https://github.com/Colorado-State-University-Stasevich-Lab/croparray/raw/main/docs/images/Croparray.gif alt="drawing" width="1000"/>
        
        ## Local installation from the Github repository
        
        * Install [anaconda](https://anaconda.org).
        
        * Clone the Github repository
        ```bash
            git clone https://github.com/Colorado-State-University-Stasevich-Lab/croparray.git
        ```
        
        * To create a virtual environment navigate to your local repository and use:
        ```bash
            conda create -n croparray_env python=3.8 -y
            source activate croparray_env
        ```
        
        * To install the rest of requirements use:
        ```bash
            pip install -r requirements.txt
        ```
        
        * To install napari use:
        ```bash
        python -m pip install "napari[all]"
        ```
        
        ## Local installation using PIP
        
        * To create a virtual environment using:
        
        ```bash
            conda create -n croparray_env python=3.8 -y
            source activate croparray_env
        ```
        
        * Open the terminal and use [pip](https://pip.pypa.io/en/stable/) for the installation:
        ```bash
            pip install croparray
        ```
        
        * To install napari use:
        ```bash
        python -m pip install "napari[all]"
        ```
        
        ## Deactivating and removing the environment
        
        * To deactivate or remove the environment from your computer use:
        ```bash
            conda deactivate
        ```
        * To remove the environment use:
        ```bash
            conda env remove -n croparray_env
        ```
        * To unistall croparray use
        ```bash
            pip uninstall croparray
        ```
        
        ## additional troubleshooting information
        * If you cannot see the package installed on your computer, try using ```pip3```. For example: 
        ```bash
            pip3 install croparray
        ```
        
        ## Installing from yml env
        
        * To creating an environment file (yml) use:
        ```sh
        source activate croparray_env
        conda env export > croparray_env.yml
        ```
        
        * ToCreate an environment from this yml file.
        ```sh
        conda env create -f croparray_env.yml
        ```
        
        ## Usage
        
        * Organizes crops and measurements of spots of interest from tif images in a convenient x-array format for reduced filesize and more open and reproducible analyses.
        * Visualizes crops of detected spots from super-resolution microscope images.
        * Calculates the best maximum projection for each crop containing a detected spot.
        * Measures intensity of detected spots within crops.
        * Calculates the correlation between two equal-length, 1D signals.
        * Saves the crop array as a netcdf file at output_direction/output_filename.
        * Integrates with Napari for fast and convenient review of crops of detected spots.
        
        ## Licenses for dependencies
        - License for [Napari](https://github.com/napari/napari): BSD-3-Clause License. Copyright (c) 2018, Napari. All rights reserved.
        
        - License for [xarray](https://github.com/pydata/xarray): Apache License. Version 2.0, January 2004. Copyright 2014-2019, xarray Developers
        
Keywords: Single-molecule image processing
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.7
Description-Content-Type: text/markdown
