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WinForms Gauge Control

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Introduction WinForm Gauge - Released 2021   WinForm Gauge is successor for AGauge control, redesigned with better structure, performance and features. Find out more  at https://www.codearteng.com/2026/09/winform-gauge.html     Original AGauge control AGauge is a gauge control for WinForms create by A.J.Bauer using GDI+. Original code was published in " Code Project - A fast and performing gauge " The version that I published here is an improved version of AGauge which contains the following changes. Download from GitHub   NOTE: We no longer actively maintaining AGauge. The development of this package is taken over by nutdotnet . Refer to his GitHub and NuGet page for details.   Improvements   Dynamic Gauge Label and Gauge Range Properties for gauge label (previously known as CapText ) and range are grouped into GaugeRanges and GaugeLabels which allow us to create any number of rang...
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Implementation of Downsampling Algorithm in MSChart Extension

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Introduction Throughout the years of development is MSChart Extension, performance issue on displaying large data size was still remain unsolved. When refreshing a chart on application, we want it to draw as fast as possible to minimize user waiting time and minimize memory consumption. When working with Microsoft Chart Control (MSChart) in WinForms, the amount of memory used and time to draw a chart gets longer with increased of data size. FastLine series have better performance but still suffer from performance hit when data size growth beyond 100K data points. Moreover, plotting large data points on screen with limited pixels caused all the data points and lines overlapped, resulting misinterpretation of information to user. Example below shows sinewave with constant frequency of 100K data points compare to downsampled chart with 500 data points. Sinewave with 100K data points Sinewave downsampled to 500 data points About Largest-Triangle-Three-Bucket (LTTB) Algorithm It's clear...
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SQLite Database Comparer - We’ve All Been There: The Frustration of Comparing and Updating Databases

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Picture this: you're staring at two SQLite files—one is your development database, the other is live in production. You know there are small schema tweaks, a handful of new rows in one table, maybe a few mismatched values. You open command-line utilities, dump contents, spin up diffs, cross-check columns, try to identify discrepancies. It takes hours. It’s slow. And every manual tweak brings the risk of human error—missing a changed schema, forgetting a row—leading to deployment failures or data inconsistency ( Stack Overflow ). You know there has to be a better way. The reality: comparing schema and data manually is tedious, error-prone, and a drain on productivity—especially when what you really need is synchronization, not guesswork. Then came that moment of clarity… The Light-bulb Moment: We Knew There Had to Be a Better Way During one late-night session maintaining multiple SQLite databases across dev, staging, and production, we realized: existing tools either only compa...
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