How does Genie 4.0 work with Python?
- Launches Python as a post NID processing analysis step.
- Python scripts can be incorporated into an Analysis Sequence File (ASF).
- Enables manipulation of CAM parameters in customized and automated ways.
- Facilitates routine extraction of specific parameters for secondary system analysis.
- Allows for custom calculations and analysis updates.
- Lets users reference additional spectral files for comparison and more.
Genie 4.0 now integrates with Python 3, aligning with the scientific community's preferred scripting tool, for more advanced and custom spectroscopy analysis. This feature allows for versatile data handling and more automated procedures.
Python Installer Tips and Suggestions
The Genie 4.0 installation requires Python, along with the Genie Python SDK installer. It's suggested that users use the 32-bit version of Python Vxx or later, and the 'python-dateutil' package.
For IDE environments, PyCharm is a popular free choice, while Anaconda is preferred for more complex tasks, albeit with a paid subscription.
Essential Python packages for Genie 4.0 include:
- NumPy: Enables mathematical and logical operations on array objects.
- SciPy: Used for scientific and technical computing, with modules for various tasks common in science and engineering.
- Pandas: Provides data structures for easy and intuitive work with relational or labeled data.
- Matplotlib: A Python library for creating static, animated, and interactive visualizations.
- Tkinter: A standard Python interface to the Tk GUI toolkit.
If you can imagine it, you can make it!
The FWHM_compare.py script in Genie 4.0 allows you to compare analysis results using both legacy and square root polynomial FWHM calibrations. Here's a simplified guide on how to use it:
- Ensure your spectrum is energy and shape calibrated with Genie 4.0.
- Ensure Python and the Genie USDK are installed on your computer.
- Copy the FWHM_compare.py file to the C:\GENIE2K\SCRIPTS folder (or equivalent).
- Open the desired spectrum in Gamma Analysis and Acquisition.
- Navigate to Analyze -> Post NID Processing -> Python Script.
- Select the FWHM_compare.py python script in the dialog that appears.
- Click on 'Execute' to run the script.
The script will run the analysis twice, once with each FWHM model, and generate a .csv file named 'fwhm_xxx.csv' (xxx denotes the current date and time). This file contains two sections:
- 'Peaks' provides Energy, Peak Area, Peak Area Uncertainty, Efficiency, and Efficiency Uncertainty for all detected peaks in both analyses, along with the % difference for peaks within a 0.25 keV tolerance.
- 'Radionuclides' lists Activity, Activity Uncertainty, and MDA for all radionuclides identified or for which an MDA was calculated in both analyses. It also includes the % difference in these values for radionuclides identified in both analyses.
The .csv file can be opened in Excel or a text editor for further review and analysis.
The example script we're highlighting shows how Python can streamline data export from processed spectra in Genie 4.0 into a .CSV file, ready for electronic delivery to a Laboratory Information Management System (LIMS). In this scenario, the script preps the data for direct loading into the CBRNResponder analytical result import template, lightening the analyst's workload during a nuclear emergency response.
CBRNResponder is a secure platform for sharing data and managing events related to chemical, biological, radiological, and nuclear (CBRN) incidents. Sponsored by Federal Emergency Management Agency among others, it's a free service for all state, local, tribal, and territorial emergency response organizations.
CBRNResponder is particularly useful during radiological or nuclear incidents. The Federal Radiological Monitoring and Assessment Center (FRMAC) utilizes this platform for laboratory analysis data reporting. Labs assisting in FRMAC responses can use this script to speed up data reporting from Genie 4.0.
Get Started with Python
Enables mathematical and logical operations on array objects.
Numpy.org Download Link
Here are 9 free resources for learning Python:
- Python.org - The official Python website provides extensive documentation, tutorials, and guides:
- SoloLearn - Provides a free Python course with interactive lessons and coding challenges:
- Google's Python Class - A free Python course by Google that includes lectures and exercises:
- Automate the Boring Stuff with Python - A free online book by Al Sweigart that focuses on using Python for practical tasks and automation:
- Python for Everybody - A free online textbook by Dr. Charles Severance covering Python fundamentals and web development:
- Link: https://www.py4e.com/
- Learn Python - Full Python course on Codecademy that covers the basics, data manipulation, and web scraping:
- Corey Schafer's Python Tutorials - YouTube tutorial series of 9 videos covering various Python topics:
- PythonProgramming.net - Offers free Python tutorials, examples, and guides for different skill levels:
- FreeCodeCamp - Provides a Python tutorial series covering Python basics, data structures, and algorithms:
These are but a few of the free resources that offer a variety of learning materials, including documentation, interactive courses, textbooks, and video tutorials. They are great starting points for beginners and valuable references for more advanced learners. Enjoy your Python learning journey!
Here's a list of 7 free Python Integrated Development Environments (IDEs):
- Robust features and tools for Python development.
- Intelligent code completion and analysis.
- Great support for web development frameworks like Django and Flask.
- Integrated version control and database tools.
- Larger memory footprint compared to lightweight IDEs.
- Comprehensive Python distribution with pre-installed scientific libraries.
- Integrated Jupyter Notebook for interactive data analysis.
- Easy package management with conda.
- Suitable for scientific computing and data science projects.
- Spyder IDE may lack the polish and advanced features of dedicated Python IDEs.
- Limited support for web development compared to specialized web-oriented IDEs.
3. Visual Studio Code (with Python extension):
- Lightweight and highly customizable IDE.
- Wide range of extensions and integrations.
- Good support for Python with the Python extension.
- Excellent debugging capabilities.
- Requires extensions for advanced Python features.
- Some Python-specific features may be less comprehensive compared to dedicated Python IDEs.
- Interactive and exploratory data analysis environment.
- Supports visualizations, documentation, and code execution.
- Popular for data science and machine learning workflows.
- Not suitable for traditional software development.
- Limited code editing features compared to dedicated IDEs.
- Lack of version control integration.
5. IDLE (Python's Integrated Development and Learning Environment):
- Lightweight and simple IDE.
- Comes bundled with Python, so no separate installation required.
- Easy to use, suitable for beginners.
- Limited features and functionality compared to more advanced IDEs.
- Lacks advanced code editing and debugging capabilities.
- Not actively developed or maintained as other IDEs.
- Lightweight IDE aimed at beginners.
- Simple and intuitive interface.
- Built-in debugger and step-by-step execution.
- Limited advanced features for experienced developers.
- Lack of extensive plugin ecosystem.
7. Eric IDE:
- Full-featured IDE with extensive Python support.
- Integrated debugger and code profiling.
- Good for larger projects.
- Interface may feel overwhelming for beginners.
- Smaller community and fewer resources compared to other IDEs.