Metadata-Version: 2.1
Name: NeuroDOT_py
Version: 1.0.1
Summary: An extensible Python toolbox for efficient optical brain mapping
Author-email: "Adam T. Eggebrecht" <aeggebre@wustl.edu>, Emma Speh <espeh@wustl.edu>, Ari Segel <ari@wustl.edu>, Yash Thacker <ythacker@wustl.edu>
Project-URL: Homepage, https://github.com/WUSTL-ORL/NeuroDOT_py
Project-URL: Issues, https://github.com/WUSTL-ORL/NeuroDOT_py/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE

NeuroDOT_py README


1. Installation:

	1. First, download Python. NeuroDOT_py is optimized for Python version 3.8.8: https://www.python.org/downloads/
	
	2. Download VSCode: https://code.visualstudio.com
	
	3. Download the Jupyter notebook extension for VSCode: launch your VS Code and type “jupyter notebook” in the extension search box. Select the first result (Jupyter) and click 'Install'.
	
	4. Install NeuroDOT_py using Pip: pip install neurodot_py


2. Geting Started
		
	1. The toolbox contains 4 folders: Data, neuro_dot, Support Files, and outputfiles/output_Images.
	
		1. The Data folder contains 10 data samples including both retinotopic mapping of visual cortex and mapping of hierarchical language processing with HD-DOT. There are also two example parameter files, 'params.txt,' and 'params2.txt' to be used with 'getting_started' (the NeuroDOT Preprocessing script).
             
		2. The neuro_dot folder contains the library, consisting of modules for each category of function involved in NeuroDOT_py (Analysis, File_IO, Light Modeling, Matlab   Equivalent Functions, Reconstruction, Spatial Transforms, Temporal Transforms, and Visualizations). There is also a function named DynamicFilter, which is used in 'getting_started.ipynb' to simplify visualizations for data pre-processing. There is also 'requirements.txt' which contains all of the necessary libraries to be installed to use NeuroDOT_py.	
		
		3. The Support Files folder contains necessary files for running NeuroDOT pipelines.
			- The A matrix file required for Reconstruction is too large to be posted on GitHub, so it can be downloaded from: https://www.nitrc.org/projects/neurodot/. Other A matrices will be added in the future.
	     
		4. The 'outputfiles' folder is created after running 'getting_started' and is where all of the images (.png) generated will be saved to.
	     
	2. 'getting_started.ipynb' is the Jupyter notebook for getting acquainted with NeuroDOT_Py. This is the file that you will open in VSCode/Jupter Notebook to run and manipulate the code. 
 

