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1-setting-up-environment.md

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Setting up an environment to run scripts/workflow packages

Objective: Try our specialized workflows when creating a large-scale collection management project

1. (option 1) Install 'git' to clone the repository

  • Windows: Download and install from Git for Windows.
  • Clone the repository by using:
git clone [URL_OF_THE_REPOSITORY]

2. (option 2) Download specific files from the repository by clicking the 'Raw' button for the file

Python Scripts for Data Processing

1. Install Python and configure PyCharm (suggested development environment, but there are many alternatives)

 

2. Install Required Libraries

  • Before running the code, ensure that you have installed the required libraries by using 'pip'
pip install package-name
  • Upgrade a package if needed
pip install --upgrade package-name
  • Make a list of all installed packages
pip list
  • Search for a package
pip search package-name
  • Check for outdate packages
pip list --outdated
  • Install packages from a requirements.txt file:
pip install -r requirements.txt

 

3. Using a Virtual Environment

  • Use a virtual environment for your Python projects to manage dependencies. This prevents potential conflicts between package versions across different projects.

  • Install 'virtualebv'

pip install virtualenv
  • Create a virtual environment
virtualenv myenv
  • Activate the virtual environment (any package you install using pip will be installed in the virtual environment, not globally)
myenv\Scripts\activate
  • Deactivate the virtual environment
myenv\Scripts\deactivate

 

Testing & Documentation

Objective: Insights into our development - both the highs and lows.

Usage:

  1. Acquire documentation files.
  2. Adapt our lessons to your needs.
  3. Test the processes.

 

Continuous Updates

  • Stay tuned! We're always enhancing our tools and workflows.
  • Don't hesitate to fork or contribute.