klotz: visualization*

Visualization is the process of representing data or information in a graphical or visual format, making it easier to understand and interpret. In the context of scientific and technical computing, visualization is often used to help analyze and communicate complex data, patterns, trends, and relationships. It can be applied in various fields such as data science, machine learning, and deep learning.

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  1. This page details the command-line utility for the Embedding Atlas, a tool for exploring large text datasets with metadata. It covers installation, data loading (local and Hugging Face), visualization of embeddings using SentenceTransformers and UMAP, and usage instructions with available options.
  2. Adafruit showcases the Raspberry Pi cam combined with the MLX90640 IR camera to create a budget fusion thermal camera. Featuring solderless components, this build is also beginner-friendly.
  3. This article details a project to display sensor data (temperature, humidity, pressure, luminosity, date, time, and IP address) on an ESP32 CYD board using LVGL and a 2.8" TFT touchscreen. The project uses a BME280 sensor and a built-in LDR on the CYD board, retrieving the current date and time from the WorldTimeAPI via WiFi. It guides users through the necessary setup, including installing libraries (LVGL, TFT_eSPI, ArduinoJson, Adafruit BME280), configuring WiFi credentials, and wiring the BME280 sensor. The code creates a responsive table to display the data, with a refresh button for updates.
  4. Git contributions e-ink display on an esp32. Show off your commit streaks with this wireless e-ink display. It uses an ESP32 to fetch and display your latest contributions.
    2025-07-05 Tags: , , , , , by klotz
  5. This article details a project to create a small desktop appliance with an E-Ink display that shows a user's GitHub activity graph. It uses an ESP32 and a 3D-printed case, configured via a web interface.
    2025-07-05 Tags: , , , , by klotz
  6. An article discussing the importance of time series databases and data visualization tools like Grafana for managing and interpreting streams of data in various applications.

    The author mentions several time series databases (TSDs) and visualization tools, focusing on their features, advantages, and some limitations. The article also provides an example of a Building Management and Control (BMaC) project that uses InfluxDB and Grafana for data visualization.

    | Database | Description | Notable Features |
    |-------------------|-------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------|
    | InfluxDB | Partially open source, with version 3 being an edge data collector. | Shard-based storage, compaction levels, time series index, optional retention. |
    | Apache Kudu | Column-based database optimized for multidimensional OLAP workloads. | Part of the Apache Hadoop ecosystem. |
    | Prometheus | Developed at SoundCloud for metrics monitoring. | Written in Go, similar to InfluxDB v1 and v2. |
    | RRDTool | All-in-one package with a circular buffer TSD that also does graphing. | Language bindings for various programming languages. |
    | Graphite | Similar to RRDTool but uses a Django web-based application to render graphs. | Web-based graphing. |
    | TimescaleDB | Extends PostgreSQL, supporting typical SQL queries with TSD functionality and optimizations. | Supports all typical SQL queries. |

    The article also discusses Grafana as a popular tool for creating dashboards to visualize time series data, mentioning its compatibility with multiple TSDs and SQL databases. It concludes by highlighting the importance of understanding one's specific needs before choosing a TSD and visualization solution.
  7. A guide to building a front-end data application using Taipy, comparing it to Streamlit and Gradio, and providing a step-by-step implementation of a sales performance dashboard.
  8. The article details the author's investigation into slow Python tool startup times. They used the `python -X importtime` feature to identify import bottlenecks and visualized the resulting data using Kevin Michel's `python-importtime-graph` tool, revealing a dense treemap of import times.
  9. QSL World offers an AI-powered tool to visualize ham radio contacts by uploading ADIF files and generating an interactive map. The tool integrates with QRZ.com for accurate data and allows users to select QSL card designs for confirmations.
  10. Grafana Labs have launched Grafana 12, bringing significant updates to its visualisation and dashboarding platform. Several new key features are now generally available, including Git Sync, dynamic dashboards, and improvements to Drilldown. A central feature is a new collection of observability-as-code tools, designed to help teams automate observability workflows.

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