morpc 0.3.1

Data managment tools used by MORPC

morpc-py

Introducion

The MORPC data team maintains a package with contains commonly-used constants, mappings, and functions to allow for code-reuse in multiple scripts. The package documentation and code is available at the morpc-py repository in GitHub.

This package is still in development but will contain the following modules:

• morpc - Main library. Includes contents which are broadly applicable for MORPC's work, including MORPC branding, region definitions and utilities, and general purpose data manipulation functions.
• morpc.frictionless - Functions and classes for working with metadata, including schemas, resources, and data packages. These are for internal processes that us the frictionless-py package. Frictionless was implemented roughly 2025 to manage all metadata and to develop workflow documentation.
• morpc.census - Constants and functions that are relevant when working with Census data, including decennial census, ACS, and PEP.
• morpc.rest_apt - Tools for working with ArcGIS Online REST API, including scripts for creating local copies as frictionless resources.
• morpc.plot - Tools for standard plots which leverage MORPC branding and data visualization best practices.
• morpc.color - Various tools for working with colors, largely implemented through morpc.plot.

Installation

A version of the package is available via pip and can be installed by a standard pip install.

$ pip install morpc

Dev Install

As the package is still in development, the best way to install it is via the pip -editable option. To do so:

1. Pull the most recent verision of the jordan_dev (branch name may change later) to a local repo.
2. Using the following command to install an editable version, replacing the path to the correct location.

$ pip install -e "C:/path/to/folder/morpc-py/"

Import the package as normal

$ import morpc

To contribute to the development branch make changes in the local repo and push them to git. When making changes to the package, you will have to re-import the package. If you are working in a Jupyter environment you will have to do this after restarting the kernel.

Documentation

See docs for documentation.

(in development) there will be a livemark site for documenatation.