0 views collected around this technical thread.
This article walks through cleaning missing hydrological data, applying various interpolation techniques, visualizing water level, flow, and sediment concentration, decomposing the time series into trend, seasonal, and residual components, and using Prophet to forecast future river sediment levels.
Learn how to perform one-dimensional interpolation in Python using SciPy's interp1d function, explore various interpolation methods such as linear, nearest, quadratic, and cubic, and visualize the results with Matplotlib to compare their fitting behaviors on sample data.
This article introduces the concept of interpolation for discrete data, demonstrates how to use SciPy's interp1d function with various methods (linear, nearest, nearest‑up, zero, quadratic, cubic), visualizes the resulting curves alongside the original points, and provides complete Python code for reproducing the plots.
This article explains how to create a smooth train animation on a web page by sampling track points, using linear interpolation for position and angle, and dynamically adjusting speed, with JavaScript code examples illustrating the implementation.