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DevOps is a model based platform that connects apps, data, and devices at lightspeed from capture and logging to analysis and deployment. It unifies Digital Twin, Simulation Twin, and Testing so development, validation, and iteration run as one continuous workflow. With Cam Journal as the first app, motorsport and sports enthusiasts can log and analyze their experiences and journeys like a diary, including pace and performance insights, making it the ideal entry point to get familiar with DevOps fast.
Cam Journal
As “Journal,” the app is intentionally built like a session diary for mp4 files, you can save motocross, karting, or Nordschleife training sessions as entries, automatically detect laps, measure lap times, jump to and replay specific laps, and track and analyze your performance over time. The current Alpha has been tested with GoPro HERO11 Black MP4 footage, achieving ~0.1 s lap-timing accuracy using the GoPro’s 10 Hz GPS.
Next, Cam Journal moves from Alpha to a stable Beta, focusing on reliability, performance, and a consistent session workflow, while expanding toward sectors within laps for more precise comparisons. The goal goes beyond sports tracking: Cam Journal supports science and validation—data-driven testing and repeatable iteration—making it a robust entry point into DevOps.
Cam Journal is the on-ramp: with Push to DataLab, it exports your session data and drops it straight into DataLab, so a ride, kart stint, or Nordschleife lap instantly becomes a structured dataset ready for analysis and iteration.
DataLab
DataLab is DevOps’ data backbone. It connects logs and live streams into one coherent signal graph—fed by Ethernet, Bluetooth, UART, CAN, files, and more. It handles import, time synchronization, mapping, routing, and persistence, so projects can reliably reconstruct panels, states, and analyses from the underlying data—repeatable, traceable, and scalable.
DataLab becomes powerful through its editors and specialized apps:
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The Code Editor is where interfaces and logic live: an IO Analyzer identifies inputs/outputs in code and models, and DataLab can interpret or interface with MATLAB, C++, Rust, Python, and whatever language is needed to build fast integrations.
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The Table editor turns raw logs into usable structure—cleaning, filtering, tagging, and managing datasets and “maps” (signals, channels, lookup tables, and characteristic curves).
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Additional apps extend the system: integrate open-source tools (e.g., parsers, simulators, plotting engines) or plug in your own custom apps to add new protocols, device drivers, model solvers, or domain-specific workflows.
The result is a single place where data, tools, and logic connect at lightspeed—from acquisition to validation, digital twin, and automated reporting.
DataAnalyzer
The Data Analyzer is DevOps’ command center for insight and visualization—built directly on the data graph that DataLab assembles from logs and live streams. It turns raw measurements into decisions fast: signals are loaded, decoded, mapped, and time‑aligned on a single timeline—across CAN/CAN‑FD, UART, Ethernet streams, files, and sensor logs.
In the Analyzer you build interactive dashboards: plots, markers, statistics, event detection, and run‑to‑run comparisons—fully tied to the project. Every result stays traceable because sources, mappings, and panel states are stored with the project and can be reconstructed exactly.
Instead of “open a CSV and hope,” the Data Analyzer delivers a robust workflow: validate data, filter and tag, downsample for speed, compare versions, and export results—up to automated reporting. That’s how DevOps becomes a high‑throughput analysis platform for development, testing, and motorsport.
phyphox® Interface
phyphox® Interface is DevOps’ wireless bridge to the mobile phyphox® app. It streams telemetry and events in real time—low-latency over Wi-Fi to your computer—staying stable with a clean time base and optional buffering for brief dropouts. In Data Analyzer, signals appear instantly as plots, markers, and live statistics; in parallel, the stream can be logged, versioned, and imported into projects so sessions can be reloaded, compared, and processed later with full reproducibility.
phyphox® Interface becomes powerful through its DataLab integration:
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As an early live connector, it feeds the signal graph (mapping, units, channel naming, filters) and can be combined with other sources—CAN, UART, Ethernet devices, Bluetooth sensors, files, or simulations.
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A project recorder stores streams consistently (time sync, metadata, device, firmware, test setup), so panels and analyses rebuild automatically.
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Open hooks enable extensions: automatic session splitting, triggers, exports to CSV/MF4, or custom workflow actions.
In short: phyphox® Interface turns a phone stream into a first-class, scalable DevOps data source.
phyphox® @ Appstore / GooglePlay
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