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  1. Disciplines

๐Ÿš— Automotive Engineering

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Last updated 1 year ago

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Bokeh

  • Description: Interactive visualization library for modern web browsers.

  • Use Case: Creating interactive plots and dashboards for automotive data analysis, such as vehicle performance metrics.

  • Documentation:

  • GitHub Repository:

CANopen for Python

  • Description: A Python package for CANopen networking used in automotive applications.

  • Use Case: Implementing and managing CANopen networks used in automotive electronics and control systems.

  • Documentation:

  • GitHub Repository:

Dash by Plotly

  • Description: A Python framework for building analytical web applications.

  • Use Case: Developing interactive web-based dashboards for visualizing automotive data like telematics and diagnostics.

  • Documentation:

  • GitHub Repository:

Matplotlib

  • Description: A library for creating static, animated, and interactive visualizations in Python.

  • Use Case: Generating charts and graphs for automotive testing data and engineering analysis.

NumPy

  • Description: Fundamental package for scientific computing with Python.

  • Use Case: Numerical computations for automotive engineering simulations and data analysis.

OpenCV

  • Description: Open Source Computer Vision Library.

  • Use Case: Image processing and computer vision for automotive applications like autonomous driving and safety systems.

Pandas

  • Description: Data analysis and manipulation library.

  • Use Case: Analyzing automotive test data, customer feedback, and manufacturing data.

Plotly

  • Description: A graphing library that makes interactive, publication-quality graphs online.

  • Use Case: Creating interactive plots and data visualizations for automotive research and development.

PyDSTool

  • Description: A Pythonic environment for dynamical systems modeling, simulation, and analysis.

  • Use Case: Modeling and simulation of automotive systems dynamics, control systems, and powertrain systems.

PyTorch

  • Description: An open source machine learning library.

  • Use Case: Developing machine learning models for automotive applications, such as predictive maintenance and autonomous driving algorithms.

Scikit-learn

  • Description: Machine learning in Python.

  • Use Case: Predictive modeling and data analysis in automotive engineering, such as failure prediction and optimization of manufacturing processes.

SciPy

  • Description: An open-source Python library used for scientific and technical computing.

  • Use Case: Technical computations in automotive engineering, including optimization algorithms and signal processing.

SimPy

  • Description: A process-based discrete-event simulation framework.

  • Use Case: Simulating automotive production lines and logistics to optimize manufacturing processes.

TensorFlow

  • Description: An end-to-end open-source platform for machine learning.

  • Use Case: Developing deep learning models for applications such as autonomous driving and vehicle recognition systems.

Vega

  • Description: A visualization grammar for creating, saving, and sharing interactive visualization designs.

  • Use Case: Advanced data visualization in automotive engineering for complex datasets, like sensor data analysis.

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Bokeh Documentation
Bokeh GitHub
CANopen for Python Documentation
CANopen for Python GitHub
Dash Documentation
Dash GitHub
Matplotlib Documentation
Matplotlib GitHub
NumPy Documentation
NumPy GitHub
OpenCV Documentation
OpenCV GitHub
Pandas Documentation
Pandas GitHub
Plotly Documentation
Plotly GitHub
PyDSTool Documentation
PyDSTool GitHub
PyTorch Documentation
PyTorch GitHub
Scikit-learn Documentation
Scikit-learn GitHub
SciPy Documentation
SciPy GitHub
SimPy Documentation
SimPy GitHub
TensorFlow Documentation
TensorFlow GitHub
Vega Documentation
Vega GitHub
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