semvec/pypi-packages huggingface dataset:
AFM-Learn 0.10.0
A Python package for visualizing and analyzing Atomic Force Microscopy(AFM) and Piezoelectric Force Microscopy(PFM) experimental data, offering tools to process, visualize, and extract meaningful insights from AFM images and measurements.
License: MIT

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|

=========
AFM-Learn
=========


    A Python package for visualizing and analyzing Atomic Force Microscopy(AFM) and Piezoelectric Force Microscopy(PFM) experimental data, offering tools to process, visualize, and extract meaningful insights from AFM images and measurements.


This Python package provides a suite of tools for the visualization and analysis of Atomic Force Microscopy (AFM) experimental data. Designed for researchers working with AFM techniques, the package simplifies the processing of raw AFM data, including height, amplitude, and phase images. The built-in functionality allows users to visualize AFM scans in image and video if temporal dependent data, apply filters for noise reduction, and extract key metrics such as roughness, feature dimensions, and ferroelectric domain structures.

Key features:
- Support for multiple AFM data formats (e.g., .spm, .afm, .ibw).
- Real-time 2D and 3D visualization of AFM data.
- Data filtering and smoothing techniques.
- Tools for extracting quantitative measurements from AFM images.
- Customizable workflows for domain structure analysis.

This package is particularly useful for materials science researchers and AFM users who want to streamline data processing and explore advanced data processing and analysis.


.. _pyscaffold-notes:

Note
====

This project has been set up using PyScaffold 4.6. For details and usage
information on PyScaffold see https://pyscaffold.org/.
Plume-Learn 1.1.1
A Python package for visualizing and analyzing plume dynamics during thin-film growth, providing tools to process, visualize, and extract meaningful insights from in-situ plume images captured during Pulsed Laser Deposition (PLD) experiments.
License: MIT

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|

===========
Plume-Learn
===========


    A Python package for visualizing and analyzing plume dynamics during thin-film growth, providing tools to process, visualize, and extract meaningful insights from in-situ plume images captured during Pulsed Laser Deposition (PLD) experiments.


This Python package offers a comprehensive set of tools for the analysis and visualization of plume dynamics data, allowing researchers to study the evolution of plume behavior during thin-film deposition processes. The package simplifies the process of handling raw image data, including image filtering, motion tracking, and plume velocity/shape analysis. Built-in functions enable users to visualize plume evolution over time in both 2D and 3D.

Key Features
------------

- Support for common image formats from in-situ plume imaging systems (e.g., .tiff, .png).
- Real-time visualization of plume frame during film growth.
- Tools for plume shape analysis, velocity estimation, and dynamic behavior tracking.
- Customizable workflows for advanced material growth analysis, including plume morphology and deposition rate correlations.

This package is ideal for materials science researchers and PLD users looking to gain insights into plume dynamics during thin-film deposition and optimize the growth process for better material quality.
    

.. _pyscaffold-notes:

Note
====

This project has been set up using PyScaffold 4.6. For details and usage
information on PyScaffold see https://pyscaffold.org/.
XRD-Learn 1.2.0
A Python package for visualizing and analyzing X-ray Diffraction (XRD) experimental data, providing tools to process, visualize, and extract meaningful insights from XRD patterns and measurements.
License: MIT

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    :alt: Project generated with PyScaffold
    :target: https://pyscaffold.org/

|

=========
XRD-Learn
=========


    A Python package for visualizing and analyzing X-ray Diffraction (XRD) experimental data, providing tools to process, visualize, and extract meaningful insights from XRD patterns and measurements.


This Python package offers a comprehensive set of tools for the analysis and visualization of X-ray Diffraction (XRD) data. Designed for researchers working with XRD techniques, the package simplifies the process of analyzing raw XRD data, including peak identification, phase analysis, and lattice parameter calculations. Built-in functions enable users to visualize diffraction patterns in both 1D (XRD scan) and 2D (RSM).

Key Features:
--------------

- Support for common XRD data formats (e.g., .xrdml).
- Real-time 1D and 2D visualization of diffraction patterns.
- Customizable workflows for advanced material characterization, including crystal structure and texture analysis.

This package is ideal for materials science researchers and XRD users looking to streamline the analysis of complex diffraction data and extract detailed structural information about crystalline materials.


.. _pyscaffold-notes:

Note
====

This project has been set up using PyScaffold 4.6. For details and usage
information on PyScaffold see https://pyscaffold.org/.