Search Results for "high level assembler"

...pandas is continuously being developed to be a fundamental high-level building block for doing practical, real world data analysis in Python, as well as powerful and flexible open source data analysis/ manipulation tool for any language.

Apache Spark™ is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. The SageMaker Spark Container is a Docker image used to run batch data processing workloads on Amazon SageMaker using the Apache Spark framework. ...

...A plug-in interface allows any user add-on data-processing and special hardware and instrument support. Latest: NC-AFM and related explorative methods as SQDM can be configured. High-Speed external PAC-PLL hardware option with digital DSP link. Based on several hardware options it supports a commercially available DSP hardware and provided also Open Source Code for all the low level signal processing tasks and instrument controls in a most flexible and adaptable manner. All latest software is available via SVN only or Live Demo/Install CD: http://www.ventiotec.de/linux/GXSM-Linux.iso

...The first goal of this project is to provide a highly intelligent analytical tool for reducing a complex chromatographic data analysis, either 1D or 2D, to just a few simple and intuitive steps. The second goal is to provide a fast and practical way of customizing or extending functionalities by ordinary users through high-level Freemat scripting. Freemat, one of open source Matlab clones, is chosen for this very purpose, because its syntax is most compatible with Matlab and it is lightweight - very suited for ordinary users. Contact xguan@jnxtec.com for support.

...It expands the value of your existing job scheduler - either Grid Engine or TORQUE PBS - through numerous functions that help you organize, submit, monitor, manage and share your informatics work. Data processing pipelines require high-level organization and parallelization of work to optimize resource utilization and decrease the time to results. q (from queue) allows complex job sequences to be efficiently assembled and managed, including dependency tracking, parallelization, and pipeline-level monitoring, error recovery, and data protection. Pipelines are constructed from modular script files, with job definitions and results stored in easily retrieved job files. ...