semvec/pypi-packages huggingface dataset:
sptlanlpy 0.0.1
Streamlining Spatial Analyses of Cicular Data Set of Properties
Keywords: python,spatial,alalysis,MSCG,stadium,Michigan Sports Consulting Group

# **spatial.py**
  
  This Library is intended to assist with SPATIAL ANALYSES For purposes of this library, a SPATIAL ANALYSIS is defined as the ANALYSIS of PROPERTIES SURROUNDING a CENTRAL LOCATION via their PROPERTY VALUE and STRAIGHT LINE DISTANCE (SLD) to the CENTRAL LOCATION within a RADIUS OF INTEREST.
## To utilize this library, a CSV FILE is required
* The file MUST be a database CONTAINING PROPERTY ADDRESSES and PROPERTY VALUES
* The file MUST be organized into TWO COLUMNS with the above data
  * These columns MUST HAVE NAMES contained in the first cell - this is how we traverse with PANDAS
##  To utilize this library GEOPANDAS, GEOPY, and HAVERSINE MUST be INSTALLED
* Either local installation or Google Colab installation via '!pip install #library name#'
## Notes on organization of spatial.py file
* The comments throughout this file explain the purpose of each function via REQUIRES, MODIFIES, and EFFECT (RME) clauses and occasional notes
*  This LIBRARY is organized via a CLASS called !!SpatialAnalysis!!. This was the best way I could think to organize all the data with abstraction
  *  Meaning a class instance MUST be declared to make calls to the functions
  *  READ the COMMENTS above !!def __init__!! to understand how to declare an instance of the class
  
 # Autors Note
 This library was a passion project made for purposes of streamlining work that the Michigan Sports Consulting Group (MSCG) at the University of Michigan does for Economic Analysis Reports. By uploading this, I hope that this work for MSCG will have an impact that outlasts my time in the club. I also hope others find use of this library. 
 
Best,
Jacob Brinkmann
 https://www.linkedin.com/in/jacob-brinkmann-567438206/
arcana-core 0.8.6
Abstraction of Repository-Centric ANAlysis (Arcana): A rramework for analysing on file-based datasets "in-place" (i.e. without manual download)
Requires Python: >=3.8
Keywords: arcana

Arcana - Core
=============
.. image:: https://github.com/ArcanaFramework/arcana-core/actions/workflows/tests.yml/badge.svg
   :target: https://github.com/ArcanaFramework/arcana-core/actions/workflows/tests.yml
.. image:: https://codecov.io/gh/ArcanaFramework/arcana-core/branch/main/graph/badge.svg?token=UIS0OGPST7
   :target: https://codecov.io/gh/ArcanaFramework/arcana
.. image:: https://img.shields.io/pypi/pyversions/arcana-core.svg
   :target: https://pypi.python.org/pypi/arcana-core/
   :alt: Supported Python versions
.. image:: https://img.shields.io/pypi/v/arcana-core.svg
   :target: https://pypi.python.org/pypi/arcana-core/
   :alt: Latest Version
.. image:: https://readthedocs.org/projects/arcana/badge/?version=latest
  :target: http://arcana.readthedocs.io/en/latest/?badge=latest
  :alt: Documentation Status


Abstraction of Repository-Centric ANAlysis (Arcana_) is Python framework
for "repository-centric" analyses of study groups (e.g. NeuroImaging
studies) built on the Pydra_ dataflow engine.

Arcana_ interacts closely with a data store (e.g. XNAT repository or BIDS dataset),
storing intermediate outputs, along with the parameters used to derive them,
for reuse by subsequent analyses.

Analysis workflows are constructed and executed using the Pydra_
package, and can either be run locally or submitted to HPC
schedulers using Pydra_'s execution plugins. For a requested analysis
output, Arcana determines the required processing steps by querying
the repository to check for missing intermediate outputs before
constructing the workflow graph.

Documentation
-------------

Detailed documentation on Arcana can be found at https://arcana.readthedocs.io

Quick Installation
------------------

Arcana can be installed for Python 3 using *pip*::

    $ pip3 install arcana-core

.. _Arcana: http://arcana.readthedocs.io
.. _Pydra: http://pydra.readthedocs.io
.. _XNAT: http://xnat.org
.. _BIDS: http://bids.neuroimaging.io/
.. _`Environment Modules`: http://modules.sourceforge.net


License
-------

This work is licensed under a
`Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License <http://creativecommons.org/licenses/by-nc-sa/4.0/>`_

.. image:: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png
  :target: http://creativecommons.org/licenses/by-nc-sa/4.0/
  :alt: Creative Commons License: Attribution-NonCommercial-ShareAlike 4.0 International

|

*Note: For the legacy version of Arcana as described in
Close TG, et. al. Neuroinformatics. 2020 18(1):109-129. doi:* `<10.1007/s12021-019-09430-1>`_
*please see* `<https://github.com/MonashBI/arcana-legacy>`_.
*Conceptually, the legacy version and the versions in this repository (>=2) are similar.
However, instead of Nipype, v2 uses the Pydra workflow engine (Nipype's successor)
and the syntax has been rewritten from scratch to make it more streamlined and intuitive.*