Metadata-Version: 2.1
Name: searchstims
Version: 2.2.0
Summary: generate images like the stimuli used in visual search experiments
Home-page: https://github.com/NickleDave/main.py
Author: David Nicholson
Author-email: nicholdav@gmail.com
License: BSD
Description: 
        [![Build Status](https://travis-ci.com/NickleDave/searchstims.svg?branch=master)](https://travis-ci.com/NickleDave/searchstims)
        [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)
        [![DOI](https://zenodo.org/badge/134479044.svg)](https://zenodo.org/badge/latestdoi/134479044)
        [![PyPI version](https://badge.fury.io/py/searchstims.svg)](https://badge.fury.io/py/searchstims)
        
        # searchstims
        
        Python package to make stimuli like those used in classic visual search experiments  
        https://en.wikipedia.org/wiki/Visual_search  
        ... but with the exact size to feed them to your favorite neural network.
        
        ![feature_search](doc/redvert_v_greenvert_set_size_4_target_present_55.png)
        ![spatial_config_search](doc/two_v_five_set_size_6_target_present_78.png)
        
        There are links to example configuration files below.
        
        For a recent review of factors influencing visual search, please see:  
        <http://search.bwh.harvard.edu/new/pubs/FiveFactors_Wolfe-Horowitz_2017.pdf>
        
        For a dataset of human subjects performing a similar visual search task, please see:
        <http://search.bwh.harvard.edu/new/data_set_files.html>
        
        ## Installation
        `pip install searchstims`
        
        If you want to download and install locally into an environment with Anaconda:
        `/home/you/Documents $ conda create -n searchstims-env python=3.6 numpy pygame`  
        `/home/you/Documents $ source activate searchstims-env`  
        `(searchstims-env) /home/you/Documents $ git clone`  
        `(searchstim) /home/you/Documents $ cd searchstims`  
        `(searchstim) /home/you/Documents/searchstims $ pip install -e .`  
        
        ## Usage
        The `searchstims` package installs itself so that you can run it from the
        command line. You will use a config.ini file to specify the visual search stimuli 
        you want the package to generate.  
        
        `/home/you/Documents $ searchstims config.ini`  
        
        Running the example script will create a folder `~/output` with visual search stimuli. 
        For more detail on the structure of `config.ini` files used with this package, see 
        [./doc/config.md](./doc/config/md).
        
        For examples of config.ini files, see [./doc/configs/](./doc/configs/).
        These examples were used in this project:  
        <https://github.com/NickleDave/visual-search-nets>
        
        ### `.json` output file
        In addition to saving visual search stimuli in the output folder, `searchstims` 
        saves information about stimuli in a `.json` output file.  This `.json` file is 
        provided to make it easier to work with the visual search image files, 
        and analyze results obtained with them. For more detail, see [./doc/json.md](./doc/json.md)
        
        ## License
        [BSD-3](./LICENSE.txt)
        
        ## Citation
        If you use this library, please cite this repository using the DOI:  
        [![DOI](https://zenodo.org/badge/134479044.svg)](https://zenodo.org/badge/latestdoi/134479044)
        
        ## Acknowledgments
        - Research funded by the Lifelong Learning Machines program, 
        DARPA/Microsystems Technology Office, 
        DARPA cooperative agreement HR0011-18-2-0019
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
