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Thermo Scientific Amira Software for life sciences and Avizo Software for materials science, renowned for powerful 3D visualization and analysis capabilities, become even more versatile and tailored to your specific needs with Python scripting. By integrating Python 3, Amira and Avizo Software allow users to leverage a wide array of scientific and analytical packages, including NumPy, SciPy, Matplotlib, PyQt, OpenCV, Keras, PyTorch, and scikit-learn.
NumPy delivers efficient multi-dimensional array handling, simplifying complex mathematical operations on large datasets.
SciPy can be used for advanced scientific computations, such as interpolation, integration, and optimization, to refine your data analysis processes and perform sophisticated image processing tasks, including filtering, transformation, and feature extraction, to gain deeper insights from your data.
Matplotlib offers interactive plotting and visualization, allowing you to present your data clearly and intuitively.
PyQT allows you to develop your own graphical user interfaces (GUIs) to enhance user interaction and streamline workflows, making the software more tailored and efficient for specific tasks.
Create multiple self-contained Python environments with their own packages within Amira and Avizo Software, ensuring compatibility and isolation for different projects.
Manage dependencies and package versions easily, preventing conflicts and ensuring that your scripts run smoothly across various projects and collaborations.
Enjoy the same performance and memory efficiency as standalone Python applications thanks to Amira and Avizo Software's tight integration with Python.
Execute Python scripts directly within the software environment, allowing for simple data exchange and interaction between Amira or Avizo Software and your custom Python code.
Elevate your workflow with Python by interacting directly with modules and adjusting their properties in real time. Turn complex module configuration into simple, repeatable automation that accelerates delivery and boosts developer productivity.
Use pre-trained CellPose models to predict instance-level segmentation and labeling in both 2D and 3D data, applicable to a variety of generalized objects.
This Xtra implements kMeans clustering on a label analysis spreadsheet. Separated objects can be clustered based on any built-in or user-customized measure group.
For Research Use Only. Not for use in diagnostic procedures.