DAS 领域的研究者和工程师终于有了一个能处理连续数据流的工具，不用再手动切片段做离线分析了。做地震监测、管道检测或声学事件标注的团队，可以直接用 FiLark 搭建从探索到部署的完整工作流。
FiLark 是一个专为分布式声学传感（DAS）设计的 Python 框架，采用“流式优先”原则统一处理数据访问、信号处理、可视化和监控。它解决了传统批处理框架无法高效处理连续、超高通道数据流的问题，支持交互式浏览任意长录音、在数据流中直接标注事件，并集成 CPU/GPU 加速的信号处理库。该框架通过统一的流抽象，使交互式开发的处理配置可直接迁移到生产流水线，无需修改。
FiLark: a streaming-first software framework for end-to-end exploration, annotation, and algorithm integration in distributed acoustic sensing
Distributed acoustic sensing (DAS) systems generate continuous, ultra-high-channel-count data streams at rates that exceed the capabilities of conventional batch-oriented analysis frameworks. As a result, essential tasks such as interactive exploration of long-duration recordings, scalable event annotation, and real-time algorithm-in-the-loop monitoring remain inadequately supported by workflows built around manually selected data segments and offline processing. This paper presents FiLark (Fiber Lark), a Python framework that applies a \emph{streaming-first} principle uniformly across data access, signal processing, visualization and monitoring for DAS. Instead of operating on manually selected data segments, FiLark presents any DAS sources-including continuous multi-file recordings-as a unified stream and builds all system components around that abstraction. An OpenGL-based ring-buffer renderer enables interactive browsing and visualization of arbitrarily long recordings with constant memory usage. An integrated annotation interface supports event labeling directly within continuous data streams, facilitating the creation of reproducible machine-learning-ready labeled datasets without offline preprocessing. The signal processing library includes temporal, spatial, spectral, and decomposition-based operators, with both CPU implementations and GPU-accelerated variants via PyTorch, alongside stateful chunked execution that preserves processing continuity and application semantics across segment boundaries. A standardized monitor interface further integrates streaming detectors and learning-based models into the visualization workflow. By sharing a common streaming abstraction across all layers, FiLark allows processing configurations and workflows developed interactively to transfer directly to scalable production pipelines without modification.