Automate COGO Point Calculations with Dynamo + Python

CadQuery Launches Version 2.7.0 to Boost Parametric 3D Modeling with Python 📌 CadQuery 2.7.0 empowers engineers with Python-driven parametric 3D modeling, shifting CAD from manual GUIs to code-centric workflows. Built on OCCT, it offers NURBS, complex fillets, and seamless DevOps integration - ideal for automation and version control. A powerful leap for those who code, not just click. 🔗 Read more: https://lnkd.in/dKJvJSEZ #Cadquery #Python #Parametric3d #Cadscripting #Opensource

🚀 Breaking CAD limits with Python! Procedurally generated a Helical Ring Geometry with twist synced inside & out using PyVista & NumPy. 🔹 Parametric Design: Instantly tweak twist angles, thickness, and teeth density—all from a single script. 🔹 Dual-Surface Symmetry: Created a complex thin-walled structure where the helical pattern is perfectly synced on both surfaces—a task that’s incredibly tedious in traditional CAD. 🔹 Simulation Ready: Optimized mesh for CFD analysis in Ansys to analyze fluid swirl and heat transfer efficiency. 💡 Imagine iterating complex mechanical designs in seconds, not hours! ⚙️✨ #Python #MechanicalEngineering #GenerativeDesign #Simulation #Ansys #ComputationalDesign #PyVista #Automation #DigitalTwin #Engineering

Stop exporting your Matplotlib figures to SVG, then importing them into Inkscape, then realizing you need to change one color. I built an extension that eliminates this entire workflow. Now you can write Python plotting code directly inside Inkscape and render it as a native SVG element — fully editable, infinitely scalable, no round-tripping required. The workflow is dead simple: → Open the extension → Write or paste your Matplotlib code → Click apply → Your figure appears as editable vector graphics Every bar, every line, every label becomes a native Inkscape object. Want to change a color? Just select it. Need to tweak spacing? Drag it. No re-exporting. No quality loss at any zoom level. You can also load external Python scripts directly, so your existing plotting code works without modification. For anyone building scientific figures, data visualizations, or publication-ready charts — this keeps everything in one place and makes iteration instant. I made a full tutorial showing the installation, all three modes, and practical examples. Link in the comments. #Matplotlib #Inkscape #DataVisualization #Python #ScienceTwitter

🚀 SM Script Library v8.0.0 – Out Now! A powerful script manager for Autodesk Maya designed to speed up and simplify your workflow. ✨ Key Features: Modern UI with Dark/Light mode Python & MEL script library One-click script execution Search & smart organization Version history + auto backup Shelf integration Built for efficiency and production use. 🎬 Check out the demo video! #Maya #AutodeskMaya #Rigging #PipelineTools #Scripting https://lnkd.in/gwbHH39R

🔧 Built a Beam Analysis Dashboard using Python Most mechanical engineering problems stop at solving numerical questions. I wanted to go a step further and convert beam theory into a usable engineering tool. I developed a simple interactive dashboard that can: • Compute Shear Force Diagram (SFD) • Compute Bending Moment Diagram (BMD) • Calculate maximum deflection • Visualize structural behavior instantly The idea was to move from: “solving problems manually” → “building systems that solve problems” ⚙️ Tech stack: Python | NumPy | Matplotlib | Streamlit 🌐 Live tool: https://lnkd.in/gXjyM7pW This is just the first version. Next step is to extend it for distributed loads, arbitrary load positions, and more generalized beam cases. #MechanicalEngineering #Python #Engineering #Simulation #Streamlit #StructuralAnalysis

I recently built a custom Python plugin using the CSI API to completely automate combined footing generation in SAFE based on ETABS reactions. It was a bit of a learning curve, but the result is a tool that: ✅ Pulls reactions directly from the model. ✅ Calculates the Load Resultant Center instantly. ✅ Imports custom AutoCAD shapes exactly where they need to be. The best part? Seeing the difference between the Geometric Center and the Resultant Center on screen. It’s a great reminder of how much the load distribution actually changes our designs. I’m sharing the code and a quick guide I wrote to explain the logic. If you’re using SAFE for foundations, feel free to grab it and save yourself a few manual steps on your next project. Let me know if you run into any issues—I’m still tweaking it! #StructuralEngineering #Automation #EngineeringTools #SAFE2000 #Python #BIM #CSIAPI #Plugin

Where Creativity Meets Code – Blender + Python This visual beautifully showcases the combination of 3D design and programming, where Python scripting is used inside Blender to generate complex geometric art. 💡 What’s happening here: Python code is used to procedurally create multiple rings Each ring is rotated and positioned to form a stunning spiral structure Blender renders the final output with lighting and materials The result: a visually appealing, mathematically-driven 3D design

🚀 Excited to share my project: Task 7 – Sensor Data Analysis with NumPy, Pandas & Visualization with KAITECH on analyzing sensor data using Python. Part 1 — NumPy: ✅ Structured NumPy arrays with custom dtypes ✅ Per-sensor averages using np.unique & np.mean ✅ Identifying the highest stress sensor ✅ Filtering readings above 36°C Part 2 — Pandas & Visualization: ✅ Converting data to pandas DataFrames ✅ Parsing timestamps as datetime objects ✅ Computing statistics using groupby ✅ Line chart — Temperature vs Time per sensor ✅ Scatter plot — Stress vs Displacement relationship #BIM #Programming

Setfos is Fast and Easy To Use Intuitive Graphical User Interface. • Easily design OPV and LED devices via the graphical user interface • Device structure visualization • Key graphs and reports are automatically generated • Run multiple simulations in parallel • Setfos multi threading ensures electrical and optical device properties can be calculated in a few seconds • Setfos includes fitting and optimization algorithms • Extract physical parameters and optimize the device layout • Easily import/export data and integrate with various frameworks (e.g., Python. Here is browser link: https://lnkd.in/g7iH-pXf

I thought State in LangGraph was just a dict you pass around. It is not. It is a reducer. When I built a pipeline that spawns 5 parallel thumbnail generators, each one returns {"thumbnail_prompts": [some_prompt]}. I expected a collision. Five workers, one key, last-write-wins. What actually happens: Annotated[list[str], operator.add] tells LangGraph to reduce those results by appending them. Each parallel worker's output gets merged into a single list automatically. No locks. No race conditions you have to manage. The type annotation is not documentation. It is a runtime instruction to the framework. This is the part the tutorial skips. You can follow a LangGraph getting-started guide entirely and never encounter Annotated with a reducer. But once you hit actual parallelism, it is the difference between a system that works and one that silently drops data. The abstraction is clean. What made it click was understanding that the type system and the execution engine are the same thing here. #Python #LangGraph #SystemDesign