Table of Contents
CAD Analytics refers to the process of extracting, analyzing, and interpreting data from Computer-Aided Design (CAD) files (like DWG, DXF). There are numerous APIs already available which can extract data from engineering drawings generally in XML or JSON format. There are also native CAD tools like AutoCAD which can also extract the data from engineering drawings in Excel format using its Data Extraction Wizard. The data which is extracted in both these scenarios seems to be complicated from the perspective of a customer.
The possible solution is to develop something that can breakdown these massive data into smaller chunks and organize it in order to increase the efficiency in the early stages of the Product Lifecycle Management (PLM). The solution that we came with is the DXF File Metadata Extractor that supports DXF files and can extract the important metadata from it. For DWG files, it need to converted to a DXF file first using any converter available online .
To get a local copy up and running follow these steps.
- Usually, pip is automatically installed if you are using Python downloaded from python.org
- pip
py -m ensurepip --upgrade
- A code editor like
is preferable.
- Open Command Prompt and clone the given Github repository:
git clone https://github.com/coding-with-parthasarathi/CAD-Analytics.git- Navigate to the project directory:
cd CAD-Analytics- In the terminal type:
python -m venv .venv- A folder named "venv" will appear in your project. This directory is where your virtual environment and its dependencies are installed.
- Activate the virtual environment:
venv\Scripts\activate.bat- Once activated, you will see your environment name in parentheses before your prompt. "(venv)"
- Install streamlit:
pip install streamlit- To deactivate the virtual environment type:
deactivate- Install ezdxf which is a Python package to create new DXF files and read/modify/write existing DXF documents:
pip install ezdxf- Repeat step 5.
- Run the cad_data.py file by typing:
streamlit run cad_data.pyContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt for more information.
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Project Link: https://github.com/github_username/repo_name