The Definitive Guide To Jython

The Deep3 Galaxy Redshift Survey: Keck/Deimos Spectroscopy in the Goods-N Field

“…Recognizing the potential legacy value of spectroscopic observations in the GOODS-N field, the Team Keck Redshift Survey (TKRS, Wirth et al 2004) utilized the DEep Imaging Multi-Object Spectrograph (DEIMOS, Faber et al 2003) on the Keck II telescope to create a publiclyavailable redshift catalog and uniform spectral database across the entire area imaged with HST/ACS by the GOODS Team. Altogether, the TKRS observed nearly 3000 sources, yielding secure spectroscopic redshifts for ∼ 1500 objects and enabling numerous studies of galaxy evolution and cosmology (e.g., Kobulnicky & Kewley 2004;Weiner et al 2006;Riess et al 2007;Juneau et al 2010).…”

Empirical Study Between Compiled, Interpreted, and Dynamic Programming Languages Applying Stable Ordering Algorithms (Case Study: Java, Python, Jython, Jpype and Py4J)

“…Proporciona a los desarrolladores Java la capacidad de escribir código productivo y dinámico utilizando una elegante sintaxis. Asimismo, permite a los desarrolladores de Python aprovechar la gran cantidad de bibliotecas y API útiles de Java que la JVM tiene para ofrecer" [13]. A pesar de las bondades de esta alternativa Jython no admite extensiones nativas escritas para CPython como NumPy o SciPy y debido a que la mayoría del código científico de Python depende fundamentalmente de dichas extensiones nativas directa o indirectamente, por lo general no se pueden ejecutar con Jython [14], es por ello que aparecen alternativas como JyNI ( Jython Native Interface) que permite cargar extensiones de CPython (la implementación nativa de Python) nativas y acceder a ellas desde Jython como si se lo hiciera desde CPython.…”

Efficient and thread-safe objects for dynamically-typed languages

“…To the best of our knowledge, only a few language implementations use object representations that support accesses from multiple threads while still providing safety guarantees. Examples are Jython [14] and JRuby [25]. Since, for instance, JavaScript does not provide shared memory concurrency, V8's hidden classes [31] as well as Nashorn's PropertyMap [16] and SpiderMonkey's object model [23] do not provide safety guarantees for shared-memory multithreading.…”

“…Jython's object model implements Python objects based on Java's ConcurrentHashMap class [14]. Thus, all object accesses are synchronized and safe with respect to our definition.…”

“…However, a large majority of dynamic language implementations does not yet provide good support for multithreading. The increasing popularity of dynamically-typed languages has led to a need for efficient language runtimes, which in turn led to the creation of alternative implementations with notable examples existing for JavaScript (SpiderMonkey [22], V8 [11], Nashorn [24]), for Python (PyPy [3], Jython [14]), as well as for Ruby (JRuby [25], Rubinius [28]). However, most of the efforts have aimed at improving sequential performance.…”