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Java EE, Java SE

Java Enterprise Edition(Java EE) 및 Java SE 비교 여러분의 운영 체제에 맞는 JDK(Java Development Kit)를 설치하면 Java 애플리케이션, Java 가상 시스템(Java Virtual Machine) 및 애플리케이션에서 일반적으로 사용되는 재사용 가능한 구성 요소 클래스 세트를 호스팅하기 위하여 컴파일러, 디버거, 도구 및 런타임 환경을 제공합니다. 이 애플리케이션 프로그래밍 인터페이스(API)는 네트워킹, I/O, XML 구문 분석, 데이터베이스 연결성, 그래픽 사용자 인터페이스(GUI) 개발 등을 위한 패키지 및 클래스를 제공합니다. 이 API는 일반적으로 Java Standard Edition(Java SE)으로 알려져 있습니다. 일반적으로 Java SE는 명령줄, GUI 프로그램 및 […]

JB183 Red Hat Application Development I: Programming in Java EE

Preface A: Introduction Section A.1: Red Hat Application Development I: Programming in Java EE Section A.2: Orientation to the Classroom Environment Chapter 1: Transitioning to Multi-tiered Applications Section 1.1: Describing Enterprise Applications Section 1.2: Quiz: Describing Enterprise Applications Section 1.3: Comparing Features of Java EE and Java SE Section 1.4: Quiz: Comparing Java EE and Java SE Section 1.5: Describing the Java Community Process Section 1.6: Quiz: Describing the Java Community Process (JCP) Section 1.7: Describing […]

h5py: reading and writing HDF5 files in Python – Christopher Lovell

If you’re storing large amounts of data that you need to quick access to, your standard text file isn’t going to cut it. The kinds of cosmological simulations that I run generate huge amounts of data, and to analyse them I need to be able access the exact data that I want quickly and painlessly. HDF5 is one answer. It’s a powerful binary data format with no upper limit on the file size. It provides […]

What is HDF5? – Python and HDF5 by Andrew Collette

What Exactly Is HDF5? HDF5 is a great mechanism for storing large numerical arrays of homogenous type, for data models that can be organized hierarchically and benefit from tagging of datasets with arbitrary metadata. It’s quite different from SQL-style relational databases. HDF5 has quite a few organizational tricks up its sleeve, but if you find yourself needing to enforce relationships between values in various tables, or wanting to perform JOINs on your data, a relational database is probably more appropriate. Likewise, […]

h5py example : Organizing Data and Metadata, Coping with Large Data Volumes

Organizing Data and Metadata Suppose we have a NumPy array that represents some data from an experiment: >>> import numpy as np >>> temperature = np.random.random(1024) >>> temperature array([ 0.44149738, 0.7407523 , 0.44243584, …, 0.19018119, 0.64844851, 0.55660748]) Let’s also imagine that these data points were recorded from a weather station that sampled the temperature, say, every 10 seconds. In order to make sense of the data, we have to record that sampling interval, or “delta-T,” […]

h5py : Quick Start Guide

1. Install 2. Core concepts An HDF5 file is a container for two kinds of objects: datasets, which are array-like collections of data, and groups, which are folder-like containers that hold datasets and other groups. The most fundamental thing to remember when using h5py is:  Groups work like dictionaries, and datasets work like NumPy arrays Suppose someone has sent you a HDF5 file, mytestfile.hdf5. (To create this file, read Appendix: Creating a file.) The very first thing you’ll need […]

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