Underscore example:
_.each([1, 2, 3], function(num) { alert(num); });| // REFERENCE UNICODE TABLES: | |
| // http://www.rikai.com/library/kanjitables/kanji_codes.unicode.shtml | |
| // http://www.tamasoft.co.jp/en/general-info/unicode.html | |
| // | |
| // TEST EDITOR: | |
| // http://www.gethifi.com/tools/regex | |
| // | |
| // UNICODE RANGE : DESCRIPTION | |
| // | |
| // 3000-303F : punctuation |
| <?xml version="1.0" encoding="UTF-8"?> | |
| <persistence xmlns="http://java.sun.com/xml/ns/persistence" | |
| xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" | |
| xsi:schemaLocation="http://java.sun.com/xml/ns/persistence | |
| http://java.sun.com/xml/ns/persistence/persistence_1_0.xsd" | |
| version="1.0"> | |
| <!-- derby --> |
| #!/bin/sh | |
| # Combined all static libaries in the current directory into a single static library | |
| # It is hardcoded to use the i386, armv7, and armv7s architectures; this can easily be changed via the 'archs' variable at the top | |
| # The script takes a single argument, which is the name of the final, combined library to be created. | |
| # | |
| # For example: | |
| # => combine_static_libraries.sh combined-library | |
| # | |
| # Script by Evan Schoenberg, Regular Rate and Rhythm Software |
| import * as mongoose from 'mongoose'; | |
| export let Schema = mongoose.Schema; | |
| export let ObjectId = mongoose.Schema.Types.ObjectId; | |
| export let Mixed = mongoose.Schema.Types.Mixed; | |
| export interface IHeroModel extends mongoose.Document { | |
| name: string; | |
| power: string; |
| interface FreeGeoIP { | |
| ip: string; | |
| country_code: string; | |
| country_name: string; | |
| region_code: string; | |
| region_name: string; | |
| city: string; | |
| zip_code: string; | |
| time_zone: string; | |
| latitude: number; |
UPDATED!!
NB This walkthrough requires a bit of experience with both PyCharm and Docker, plus a *NIX dev env.
At the moment of writing, PyCharm doesn't support development process with a remote Docker host, which is quite an obstacle for serious and comfortable data-analysis work. Fortunately, present-day PyCharm does support "remote interpreters" via SSH, so here I describe how to setup a containerised project appropriately. This takes a little more than 5 minutes, but in the end you will have the comfort of the local PyCharm plus security and performance of a remote host (plus the evident interoperability of Docker).