This introduction to SplineCloud shows how the platform helps engineers and scientists make knowledge accessible and reusable. You’ll learn how to organize your data in repositories, extract clean datasets, build curve fits, and access them directly from your code. The video offers a quick tour of the interface and demonstrates a real-world example of reusing data in design calculations.
This video introduces SplineCloud’s plot digitizer, a tool for extracting data from graph images. This tool is integrated into the platform and can be handy in turning static plots into shareable and reusable datasets. In this video, you’ll learn how to define coordinates, select data points, organize datasets and subsets, fit curves to data ranges, export and reuse results directly in your code.
This guide shows how to use SplineCloud’s curve-fitting tools to build smooth, accurate, and reusable regression models based on parametric splines. You’ll learn how to fit curves to data using automatic fitting methods, adjust fitting parameters, fine-tune spline curves, and share and reuse them in your Python or MATLAB code.
In this video, you’ll explore SplineCloud’s Fine-Tuning mode to address advanced curve fitting problems with the help of parametric splines. You’ll discover how parametric splines offer greater flexibility and precision than traditional polynomial fitting, and how SplineCloud makes spline models reusable in Python and MATLAB. Whether working with statistical or experimental data, you’ll learn how to create smoother, more accurate, and reusable models.
This tutorial compares curve fitting in Python using NumPy/SciPy with SplineCloud’s approach. You’ll see the limits of hard-coded methods, explore advanced features like fine-tuning, and learn how SplineCloud simplifies the curve-fitting workflow and improves the model sharing process.
leave us a message here or write to info@splinecloud.com