CommonSize | Android通用屏幕适配方案

by licheedev Java Version: 1.3.1 License: No License

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | CommonSize Summary

CommonSize is a Java library. CommonSize has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Android通用屏幕适配方案

Support

Quality

Security

License

Reuse

Support

CommonSize has a low active ecosystem.

It has 52 star(s) with 11 fork(s). There are 5 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 3 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CommonSize is 1.3.1

Quality

CommonSize has 0 bugs and 0 code smells.

Security

CommonSize has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

CommonSize code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CommonSize does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

CommonSize releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

CommonSize saves you 162923 person hours of effort in developing the same functionality from scratch.

It has 166968 lines of code, 52 functions and 138 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed CommonSize and discovered the below as its top functions. This is intended to give you an instant insight into CommonSize implemented functionality, and help decide if they suit your requirements.

write content .
Main entry point .
Copy file to destination
Parses a sw list of sws from a file .
Parse output configuration
Copies a directory .
generate the module name
Initialize the tv .
Gets a blank string .
Apply the text size to a text view .

Get all kandi verified functions for this library.

CommonSize Key Features

No Key Features are available at this moment for CommonSize.

CommonSize Examples and Code Snippets

生成器配置 generator_config.properties

Java

Lines of Code : 27

License : No License

Copy

# 输出配置，格式为w*n，其中w为参考宽度，美工的图给多少就填多少，n为dimen资源文件中，dimen条目的个数
# 输出配置可以填多个，分别以英文逗号,隔开
output_config=750*1000,750*1334,1080*1920
# 模块名字规则，{w}为上面的参考宽度，{n}为上面的条目数目
module_name_reg=common_size_w{w}_n{n}
# 缩进空格长度
intent_length=4
# 正数尺寸文件名
normal_file_name=nor

CommonSize ,使用

Java

Lines of Code : 15

License : No License

Copy

allprojects {
    repositories {
        ...
        maven { url 'https://jitpack.io' }
    }
}

dependencies {
        // 随便选一个，没有就按照下面配置重新生成自己要的
        // 公司美工喜欢做iphone(750*1334)的图，就任意选一个w750的就行了
        // 本人所在公司的广告机项目做的图是1080P的，所以有个w1080的配置

CommonSize ,原理 smallestWidth

Java

Lines of Code : 3

License : No License

Copy

// 获取最小宽度的代码
Configuration config = getResources().getConfiguration();
int smallestScreenWidthDp = config.smallestScreenWidthDp;

Community Discussions

Trending Discussions on CommonSize

Will using %chin% to subset character columns of an auto-indexed data.table ever improve speed?

QUESTION

Will using %chin% to subset character columns of an auto-indexed data.table ever improve speed?

Asked 2018-Jul-31 at 16:13

TLDR: When using more recent versions of data.table that use auto-indexing, is there any benefit from using %chin% to subset a data.table on character columns?

In the past, using %chin% from data.table in place of %in% when subsetting on character vectors resulted in a significant speed-up. In newer versions of data.table, secondary indices are automatically created on non-key columns when sub-setting. The creation and usage of these indices appears to make any speed difference between %chin% and %in% irrelevant.

Going forward, are there any cases where using %chin% to subset a data.table will still improve speed, or can I just use %in% in the future?

Update: The conversation on PR#2494: Better subsetting optimization for compound queries seems to support an understanding that when evaluated in the data.table calling environment, the execution methods of %chin% have been fundamentally changed.

For cases where the column used to subset the table more than once, performance will be drastically increased by auto-indexing, but when only used a single time (and thus not benefiting from the time spent generating the index), turning auto-indexing off will sometimes give marginally faster results.

I'll leave this open for a couple days, but I may flesh this out into an answer for the sake of posterity.

Data Generation and Benchmarking

The data generated is made up of a randomly ordered combination of two unbalanced samples:

10 million single character strings, 26 possible unique values
1 million four character strings, 456,976 possible unique values

The intent here is to be representative of non-normal variables that are dominated by a few common values, but have numerous less common possibilities.

...

ANSWER

Answered 2018-Jul-31 at 16:13

Providing an answer here for the sake of closure: I've done my best to skim through the relevant commits on Github, but there's certainly a possibility I'm missing some of the finer nuances. If any of the contributors would like to make some clarifications, I'd be happy to update here.

The conversation on PR#2494: Better subsetting optimization for compound queries seems to support an understanding that when evaluated in the data.table calling environment, the execution methods of %chin% have been fundamentally changed.

When sub-setting a data.table in cases where the same column will be used to subset the table multiple times, performance can be drastically increased by auto-indexing, so stick with the default options: options(datatable.auto.index = TRUE) and options(datatable.use.index = TRUE). In this case, using %chin% will not result in any performance improvement.
If performance is critical and you know that you will only be sub-setting on a given column a single time (and thus not benefiting from the time spent generating the index), turning auto-indexing off with options(datatable.auto.index = FALSE)and can give marginally faster results. You can still manually create indexes and keys as always if you will be performing repetitive subsets, but the burden of optimization will rest on the user to make appropriate use of setkey() and setindex(). When auto-indexing is turned off, using %chin% will be faster than %in%.
When testing whether an element is present in a character vector outside of the data.table calling environment , %chin% is still faster than %in%

Source https://stackoverflow.com/questions/49385259

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install CommonSize

You can download it from GitHub.
You can use CommonSize like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the CommonSize component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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