Saravanan666/pypi-org huggingface dataset:
Package: hehey-hcache
hehey-hcache 组件介绍hehey-hcache 是一个python 数据缓存工具组件依赖以及版本要求python >= 3.5安装直接下载:命令安装：pip install hehey-hcache基础文件以目录参数配置conf={'keyPrefix':"yi",'defaultStorage':'memcached','customStorages':{# redis 远程存储 配置'redis':{'clazz':'hcache.storages.RedisStorage.RedisStorage','red':{'host':'127.0.0.1','port':6379,'db':1,'password':""}},# file 文件配置'file':{'clazz':'hcache.storages.FileStorage.FileStorage','cachePath':'/home/hehe/www/cache','dirLevel':2,},# 本地内存配置'local':{'clazz':'hcache.storages.LocalStorage.LocalStorage','options':{'mode':'lru',}},# memcached远程存储配置'memcached':{'clazz':'hcache.storages.MemcachedStorage.MemcachedStorage','mc':{'servers':['127.0.0.1:11211'],}}},}# 支持四种存储,redis,file,memcached,local(进程内存)# keyPrefix: key 前缀# defaultStorage: 默认存在类型,比如redis基本示例快速使用fromhcache.cacheimportCacheManager;conf={'keyPrefix':"yi",'defaultStorage':'memcached','customStorages':{# redis 远程存储 配置'redis':{'clazz':'hcache.storages.RedisStorage.RedisStorage','red':{'host':'127.0.0.1','port':6379,'db':1,'password':""}},# file 文件配置'file':{'clazz':'hcache.storages.FileStorage.FileStorage','cachePath':'/home/hehe/www/cache','dirLevel':2,},},}hcache=CacheManager(conf)# 设置缓存hcache.set("accountInfo",{"realName":"hehe","stauts":1})hcache.set("name","hahahxxxx",expire=20)# 获取缓存hcache.get("name")hcache.get("accountInfo")批量设置/批量获取fromhcache.cacheimportCacheManager;conf={'keyPrefix':"yi",'defaultStorage':'memcached','customStorages':{# redis 远程存储 配置'redis':{'clazz':'hcache.storages.RedisStorage.RedisStorage','red':{'host':'127.0.0.1','port':6379,'db':1,'password':""}},# file 文件配置'file':{'clazz':'hcache.storages.FileStorage.FileStorage','cachePath':'/home/hehe/www/cache','dirLevel':2,},},}hcache=CacheManager(conf)# 批量设置datas={"account":{"realName":"hehe"},"address":{"cityId":1,"userId":20}};hcache.mset(datas,expire=20)# 批量获取hcache.mget("account","address")# 或hcache.mget(["account","address"])检验key 是否存在fromhcache.cacheimportCacheManager;conf={};hcache=CacheManager(conf)ifhcache.exists("account"):print("yes")else:print("no")直接使用存储set/getfromhcache.cacheimportCacheManager;conf={'keyPrefix':"yi",'defaultStorage':'memcached','customStorages':{# redis 远程存储 配置'redis':{'clazz':'hcache.storages.RedisStorage.RedisStorage','red':{'host':'127.0.0.1','port':6379,'db':1,'password':""}},# file 文件配置'file':{'clazz':'hcache.storages.FileStorage.FileStorage','cachePath':'/home/hehe/www/cache','dirLevel':2,},},}hcache=CacheManager(conf)hcache.file.set("accountInfo",{"realName":"hehe","stauts":1})hcache.file.set("name","hahahxxxx",expire=20)# 获取缓存hcache.file.get("name")hcache.file.get("accountInfo")装饰器注解缓存数据fromhclient.clientimportClient;
Package: heiafr-hydrocontest-sensor_api
Sensor API for the Raspberry PiThe Sensor API for the Raspberry Pi is a web service that give access to environmental
information of a Raspberry Pi. It uses the sensors of theSense Hatas well as the
internal CPU thermometer.It returns the information as a JSON structure:{
  "C": 50.464, 
  "H": 39.45915603637695, 
  "P": 941.78369140625, 
  "T": 34.720176696777344
}C is the CPU Temperature (in °C)H is the Humidity (in %rH)P is the Barometric Pressure (in Millibars)T is the Ambient Temperature (in °C)InstallationInstall the required software:sudo apt update
sudo apt install sense-hat libatlas-base-dev libopenjp2-7-dev gunicorn3Install the app:sudo pip install heiafr-hydrocontest-sensor_apiRunningIf you want to test the service you can just start it localy using gunicorn:gunicorn3 heiafr.hydrocontest.sensor_api:appYou can now go tohttp://127.0.0.1:8000and check the result.In a production environment, you would rather start the service using systemd and use a reverse proxy (e.g. Nginx) in front of it.You can use this systemd service file (you can name itheiafr-hydrocontest-sensor_api.service):[Unit]
Description=HEIAFR Hydrocontest Sensor API
After=network.target

[Service]
User=root
Group=root
ExecStart=/usr/bin/gunicorn3 --bind=0.0.0.0:8081 heiafr.hydrocontest.sensor_api:app

[Install]
WantedBy=multi-user.targetThis is a possible Nginx configuration:location /sensors {
    proxy_set_header    Host $host;
    proxy_set_header    X-Real-IP $remote_addr;
    proxy_set_header    X-Forwarded-For $proxy_add_x_forwarded_for;
    proxy_set_header    X-Forwarded-Proto $scheme;
    proxy_pass          http://localhost:8081/;
    proxy_read_timeout  90;
}
Package: heic-image-converter
Convert those Pesky.HEICImages to Something More Useful!i-Device images often come in.heicimage file format, but these don't translate well to other uses...I had a bunch of downloaded.heicfiles but wanted to convert them to.pngformat so I wrote this converter!Notesno suitable package was found to convert.heic➡️.jpgon Windows.panoramic or stretched images may not convert properly.skips already-converted files to prevent redundancyUsagecurrently working on a PYPI release...Install required packagespip install pillow_heifpip install pillow(or use defaultPIL- legacy)seerequirements.txtDownload the repo.zipObtain thepath to the pythonmain.pyprogramObtain thepath to the.heicimage folderUse desiredflagsExamplesSet the destination folder with--destAutomatically delete converted files with-delFlagsdouble-hyphen '--' flags take argumentssingle-hyphen '-' flags are booleansFlagOptionsDescription--topngsets the desired type to convert the.heicfiles to.--destpath/to/saveDestchoose the save location for the converted files.-delnonedelete the original.heicfile after successful conversions.if you have comments / suggestions / bug fixes, please make a github issueherethanks for checking this project out :)
Package: heic-to-jpeg-converter
HEIC TO JPGConverts all pictures in a given folder from HEIC to JPG. Stores the new photos with the same name in/convertedfolder in the same directory unless target folder provided in flags.Thanks tothis link.Installing prerequisitesInstall libheif via HomebrewHomebrew/bin/bash-c"$(curl-fsSLhttps://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"Libheifbrewinstalllibheifpipinstallheic-to-jpeg-converterUsageConvert everything in current folder and store in/convertedjpeg-converterconvertFind all files in the Downloads directory and save the converted pictures in current directoryjpeg-converterconvert-s~/Downloads-t.Only list the files in the Downloads directory that will be convertedjpeg-converterconvert-s~/Downloads-lTesting LocallyCreate a virtualenvpython3-mvenvenvActivate the virtual environmentsourceenv/bin/activateUse scriptdevelopment.sh
Package: heic-to-jpg
heic-to-jpg📸CLI tool for convertingHEICimages tojpg.Essentially, this is just a thin wrapper around imagemagick'sconvertcli.Motivation: When transfering images from an iPhone to a Mac via airdrop, they are being transfered asHEIC. Oftentime though, another format (such asjpg) is required to further make use of those images.👉 ⚠️ Please note thatimagemagickis a prerequisite for this to work.👉 ⚠️ Please note that this has been tested on macOS only. There might be issues on other operating systems (Linux,Windows).installationpip install heic-to-jpgusageheic-to-jpg -s ~/path/to/source [--keep] [--debug None|Trace|All]orheic2jpg -s ~/path/to/source [--keep] [--debug None|Trace|All]~/path/to/sourcecan both be a directory or a single.HEICfile. Without the--keepflag, the original file is deleted after conversion.
Package: heidelberg-metadata-gui
heidelberg-metadata-guiThis project has been funded by SFB 1158 (https://www.sfb1158.de/, Project identifier (German Research Foundation (DFG) - project number 255156212)Metadata standardization GUI. Makes it easy to edit metadata related to neurophysiological experiments.InstallIt is recommended that you installheidelberg-metadata-guiin a clean environment, for example with conda:$ conda create -n env_heidelberg python=3.7 pipIn your clean environment, just pip install:$ pip install heidelberg-metadata-guiTo install from the repository:$ pip install git+https://github.com/catalystneuro/heidelberg-metadata-guiUsageNavigate to the directory where your dataset is stored, then run from command line:$ metadata-guiOn your browser, navigate tolocalhost:5000.You can run metadata-gui with optional arguments, for example, to start it with a specific schema file and run the server on a specific port:$ metadata-gui --schema_path /home/user/my_schema.json --port XXXXRunning on dockerBuild docker with:docker build -t heidelberg:latest .run the docker with (this will reference a local folder to docker):docker run -it -p 5000:5000 -v /host/path/to/filesFolder:/usr/src/heidelberg_metadata_gui/files <image_id>Your referenced local folder will be in the files folder of the docker
Package: heidi
A python module for conversation and text summarization and much more
exciting features.Featuresprovided by this module:Text Segmentationusing:TextTilingwithBlock ScoreTextTilingwithVocabulary introductionConversational summarizationusing:Cluster RankText summarizationusing :TextRankRandomInstallationMake sure you havePython2.7/3.3+ andpip(Windows,Linux)
installed. Run simply (preferred way):$[sudo]pipinstallreadlessOr for the latest version in development:$[sudo]pipinstallgit+git://github.com/adichat/read-less.gitReadLess APIYou can use readless like a library in your project.For quickly summarizing a conversation using ClusterRank algorithm:# -*- coding: utf8 -*-fromreadless.Summarizationimportclusterranksummarizer=clusterrank.ClusterRank()pathToFile="C:/conversation.in"summary=summarizer.summarizeFile(pathToFile)For segmenting a text using TextTiling algorithm:# -*- coding: utf8 -*-fromreadless.Segmentationimporttexttilingsegmentation=texttiling.TextTiling()pathToFile="C:/conversation.in"segmentedText=segmentation.segmentFile(pathToFile)For a detailed list of other API functionalities, see**ReadLess
Documentation**.ContributionsAll contributions are welcomed. This module is in development and there
are several scopes of improvement. Tests are to be implemented along
with other Summarization algorithms with support for web page
summarization. For upcoming features, seeFuture developments.LICENSE
Package: heiDPI
nDPIdDocker Image for deep packet inspection. As described innDPId, we split the image into producer and consumer image for a more generic purpose. For the producer, the image starts the UNIX and UDP socket and nDPId respectively. Via environment variables, users can adapt the nDPId daemon and nDPIsrvd. As by now, we support all current nDPId parameters.Live NotebookLatest ReleaseSupported VersionsProject LicenseContinuous IntegrationGetting StartedInstall using PyPi:pipinstallheiDPIUse the CLI for quick usage:> heiDPI -h
usage: heiDPI [-h] [--host HOST | --unix UNIX] [--port PORT] [--write WRITE] [--config CONFIG] [--show-daemon-events SHOW_DAEMON_EVENTS] [--show-packet-events SHOW_PACKET_EVENTS] [--show-error-events SHOW_ERROR_EVENTS] [--show-flow-events SHOW_FLOW_EVENTS]

heiDPI Python Interface

options:
  -h, --help            show this help message and exit
  --host HOST           nDPIsrvd host IP (default: None)
  --unix UNIX           nDPIsrvd unix socket path (default: None)
  --port PORT           nDPIsrvd TCP port (default: 7000)
  --write WRITE         heiDPI write path for logs (default: /var/log)
  --config CONFIG       heiDPI write path for logs (default: /home/smachmeier/projects/emcl/heiDPI/config.yml)
  --show-daemon-events SHOW_DAEMON_EVENTS
                        heiDPI shows daemon events (default: 0)
  --show-packet-events SHOW_PACKET_EVENTS
                        heiDPI shows packet events (default: 0)
  --show-error-events SHOW_ERROR_EVENTS
                        heiDPI shows error events (default: 0)
  --show-flow-events SHOW_FLOW_EVENTS
                        heiDPI shows flow events (default: 0)PrerequisitiesIn order to run this container you'll need docker installed.WindowsOS XLinuxUsagePull images:dockerpullstefan96/heidpi-producer:main
dockerpullstefan96/heidpi-consumer:mainRun producer and consumer separately from each other using UDP socket:dockerrun-p127.0.0.1:7000:7000--nethoststefan96/heidpi-producer:main
dockerrun-eHOST=127.0.0.1--nethoststefan96/heidpi-consumer:mainor use thedocker-compose.yml:docker-composeupAdditionally, you use a UNIX socket:dockerrun-v${PWD}/heidpi-data:/tmp/--nethoststefan96/heidpi-producer:main
dockerrun-v${PWD}/heidpi-data:/tmp/-v${PWD}/heidpi-logs:/var/log-eUNIX=/tmp/nDPIsrvd-daemon-distributor.sock--nethoststefan96/heidpi-consumer:mainEnvironment VariablesConsumerVariableTypeDefaultUNIXstringHOSTstringPORTint7000JSON_PATHstring/var/log/nDPIdsrvd.jsonSHOW_ERROR_EVENTSint0SHOW_DAEMON_EVENTSint0SHOW_PACKET_EVENTSint0SHOW_FLOW_EVENTSint1MAX_BUFFERED_LINESint1024Config fileYou can change the default configuration by mounting a config file/usr/src/app/config.yml:flow_event:ignore_fields:[]flow_event_name:-update-end-idle-detectedfilename:flow_eventthreads:25LicenseThis project is licensed under the GPL-3.0 license - see theLICENSE.mdfile for details.
Package: heif-convert
heif-convertMulti-Platform command line tool written in Python to convert HEIF images.📝 Table of ContentsAboutInstallationUsageArgumentsLibrariesSupported operating systemsLicense📕 Aboutheif-convert is a multi-platform tool written in Python to convert High Efficiency Image File (HEIF) images to jpg, png, webp, gif, tiff, bmp, or ico.heif-convert is designed to make HEIF batch conversion easy.⚙️ InstallationPython repositoryThe easiest way to get heif-convert is through the pypi.org repository. Install it by running the following command:pipinstallheif-convertBuilding from sourceTo install heif-convert from source, clone this repository and runpip install .as follows:gitclonehttps://github.com/NeverMendel/heif-convert.gitcdheif-convert
pipinstall.Docker imageTo pull heif-convert Docker image, run:dockerpullnevermendel/heif-convertUsageheif-convert can be used from the command line by invoking theheif-convertcommand.Convert an HEIF image to a JPG image:heif-convertinput.heic-fjpgConvert all HEIF images in the current folder to JPG images:heif-convert*.heic-fjpgDocker imageConvert an HEIF image to PNG using the Docker image:dockerrun-v"$(pwd)":/usr/app/out--rmnevermendel/heif-convertinput.heic-fjpgArgumentsusage: heif-convert [-h] [-o OUTPUT] [-p PATH]
                    [-f {jpg,png,webp,gif,tiff,bmp,ico}] [-q QUALITY] [-n] [-v]
                    [-vv] [-V]
                    input [input ...]

Command line tool to convert HEIF images

positional arguments:
  input                 HEIF input file(s)

options:
  -h, --help            show this help message and exit
  -o OUTPUT, --output OUTPUT
                        output file name
                        defaults to original file name (default: '{name}')
  -p PATH, --path PATH  output file path
                        defaults to original file path (default: '{path}')
  -f {jpg,png,webp,gif,tiff,bmp,ico}, --format {jpg,png,webp,gif,tiff,bmp,ico}
                        output format (default: jpg)
  -q QUALITY, --quality QUALITY
                        output quality, integer [0, 100] (default: 90)
  -n, --no-exif         Do not include EXIF metadata in the converted image
  -v, --verbose         enable verbose logging
  -vv, --extra-verbose  enable extra verbose logging
  -V, --version         show program's version number and exitLibrariesheif-convert uses the following libraries:Pillowpillow_heifSupported operating systemsheif-convert works on Linux, Mac OS and Windows systems. For further information refer to thepillow_heifrepository.LicenseMIT License
Package: heif-image-plugin
heif-image-pluginSimple HEIF/HEIC images plugin forPillowbase onpyhieflibrary.Originally based on thepyheif-pillow-openercode from Christian Bianciotto.InstallationYou can installheif-image-pluginfromPyPI:pip install heif-image-pluginHow to useJust import once before opening an image.fromPILimportImageimportHeifImagePluginimage=Image.open('test.heic')image.load()How to contributeThis is not a big library but if you want to contribute is very easy!clone the repositorygit clone https://github.com/uploadcare/heif-image-plugin.gitinstall all requirementsmake initdo your fixes or add new awesome features (with tests)run the testsmake testcommit in new branch and make a pull requestChangelog0.6.1Added compatibility with Pillow 10.1+0.6.0Minimal supported pyheif is 0.7.1Addeddownsamplingparameter for saving. Works only withsubsampling== 2.Transformations support updated to the latest libheif and pyheif0.5.1Fixed HEIF saving in '1' mode0.5.0Added HEIF saving support ifheif-encis installed (part of libheif)FixedHeifImageFile.verify()callExtensions .heic, .avif, .heif, .hif are handled by the plugin0.4.0Bypass some decoding errors whenImageFile.LOAD_TRUNCATED_IMAGESis True.0.3.2Depends on latest pyheif.0.3.1! This version requires pyheif withpyheif.openAPI. As of 2021.11.25 this API
isn't released and is in pyheif's master. Seeinstall-pyheif-master-pillow-latesttarget in theMakefileto install it.Fixed potential vulnerability with arbitrary data in exif metadata.0.3.0! This version requires pyheif withpyheif.openAPI. As of 2021.11.25 this API
isn't released and is in pyheif's master. Seeinstall-pyheif-master-pillow-latesttarget in theMakefileto install it.pyheif.openAPI is used for lazy images loading.Fixed an error when the plugin tries to load any ISOBMFF files.AVIF files should work before, but now this is official.Patched versions ofpyheifandlibheifwith exposed transformations is supported.
In this case opened image isn't transformed on loading and orientation is stored
in EXIFOrientationtag like for all other image formats.
This is faster and consumes less memory.0.2.0No need to register, works after import.Fillinfo['icc_profile']on loading.Close and release file pointer after loading.Deconding without custom HeifDecoder(ImageFile.PyDecoder).
Package: height2obj
hight2objConverts a heightmap to a 3d mesh.Usagehight2obj SOURCE DESTINATION SCALEParametersSOURCE: Filename of the source hightmap. This must be a 8 bpp greyscale bitmap-type image. PNG is tested an recommended.DESTINATION: Filename of the destination file into which the 3d data is written.SCALE: Scale factor for the z-coordinate. This value is the maximum height of the resulting mesh relative to the short side side of the SOURCE image.RemarksThis script uses the pillow library to load the image. Output format Wavefront OBJ.This only creates a 3d surface, no solid block.Make shure to install this package via pip to allow comfortable use in console.
Package: heiko
HeikoHeiko is a frontend forMaaSand a client to be run in K4CGIt provides functionality to run an accounting system on terminal basis
on really old hardware.InstallHeiko is a pypi package that you can install via pippip install heikoFor install from local git repository via gitpip install .For development, we need some extra packages, maintined in the target
"dev"pip install .[dev]RunheikoConfigureYou may want to configure your instance properly. For this seeconfig.template.yamland copy it toconfig.yamlDevelopment:swagger-clientMaaS (Matomat as a Service) API definitionThis Python package is automatically generated by theSwagger
Codegenproject:API version: 0.5.2Package version: 1.0.0Build package: io.swagger.codegen.languages.PythonClientCodegenRequirements.Python 3.4+
Package: heimarobot
Heimarobot Python LibraryLicenseThe code of heimarobot Python Library is released under the MIT License. There is no limitation for both academic and commercial usage.本项目具备如下推理功能：人脸检测人脸识别人脸年龄与性别识别人脸106个特征点换脸B站演示地址:https://space.bilibili.com/485304569安装pip安装pipinstallheimarobot==0.0.1若安装失败，请指定安装路径pipinstallheimarobot==0.0.1-ihttps://pypi.org/project导入权重文件在用户目录下新建.heimarobot文件夹C:\Users\KAI\.heimarobot从百度网盘中下载权重文件,并解压释放进去功能演示人脸检测fromheimarobotimportFaceDetectionfromheimarobotimportdraw_facesimportcv2ascvimportnumpyasnpif__name__=='__main__':face_detection=FaceDetection()img=cv.imread('images/trump1.jpg')faces=face_detection.predict(img)dst=draw_faces(img,faces)cv.imshow('img',dst)cv.waitKey(0)人脸识别fromheimarobotimportFaceRecognitionimportcv2ascvimportnumpyasnpif__name__=='__main__':face_recognition=FaceRecognition()trump1=cv.imread('images/trump1.jpg')trump2=cv.imread('images/trump2.jpg')# 获取人脸特征feats1=face_recognition.predict(trump1)feats2=face_recognition.predict(trump2)sim=face_recognition.compute_sim(feats1,feats2)# 根据相似度,输出结果ifsim<0.2:conclu='they are not the same'elifsim>=0.2andsim<0.28:conclu='they are looks like the same people'else:conclu='they are the same people'print(sim,conclu)cv.imshow('trump1',trump1)cv.imshow('trump2',trump2)cv.waitKey(0)人脸年龄与性别识别fromheimarobotimportFaceAttributefromheimarobotimportdraw_facesimportcv2ascvimportnumpyasnpif__name__=='__main__':face_attr=FaceAttribute()img=cv.imread('images/trump1.jpg')# 获取人脸特征faces=face_attr.predict(img)# 1 表示男性， 0，表示女性print(faces[0])dst=draw_faces(img,faces)cv.imshow("images/1.jpg",dst)cv.waitKey(0)人脸106个特征点fromheimarobotimportFaceLandmarkimportcv2ascvimportnumpyasnpfromheimarobotimportdraw_faces,draw_landmarksif__name__=='__main__':face_attr=FaceLandmark()trump1=cv.imread('images/trump1.jpg')# 获取人脸特征106个特征点faces=face_attr.predict(trump1)retimg=draw_landmarks(trump1,faces)cv.imshow('trump1_landmark',retimg)cv.waitKey(0)换脸fromheimarobotimportFaceSwapperimportcv2ascvimportnumpyasnpif__name__=='__main__':face_swapper=FaceSwapper()trump1=cv.imread('images/trump1.jpg')target=cv.imread('images/1.jpg')sourceFace=face_swapper.get_source_face(trump1)dst=face_swapper.predict(sourceFace,target)cv.imshow("trump1",trump1)cv.imshow("1",target)cv.imshow("dst",dst)cv.waitKey()若运行代码出现一下错误，则是opencv-python-headless与opencv-python版本不匹配的原因cv2.error:OpenCV(4.7.0)D:\a\opencv-python\opencv-python\opencv\modules\highgui\src\window.cpp:1272:error:(-2:Unspecifiederror)Thefunctionisnotimplemented.RebuildthelibrarywithWindows,GTK+2.xorCocoasupport.IfyouareonUbuntuorDebian,installlibgtk2.0-devandpkg-config,thenre-runcmakeorconfigurescriptinfunction'cvShowImage'卸载opencv-python-headless,重新安装pipinstallopencv-python-headless==4.6.0.66pipinstallopencv-python==4.6.0.66pipinstallopencv-contrib-python==4.6.0.66
Package: heimdall-android
[TOC]##简介利用工具，即可对android移动端APP进行快速的性能测试(CPU、内存)、开发日志的查看、Crash日志查看统计等
测试任务就是保证功能的正常，并且不会有遗留的内存对象，造成内存泄漏问题##环境要求macOSPython3.6及以上android adb##使用
##测试流程
###0. 前提
使用时，通过USB线连接手机和电脑即可。###1. 命令测试'''
usage: heimdall3 [-h] [-d DEVICE_ID] [-s SAVE_PATH] [-p PACKAGE_PROCESS]
             [-t1 PEFORMANCE_INTERAL] [-t2 HROF_INTERAL]
optional arguments:
      -h, --help            show this help message and exit
      -d DEVICE_ID, --device_id DEVICE_ID
                            Set device id to memory
      -s SAVE_PATH, --save_path SAVE_PATH
                            Set path to save results
      -p PACKAGE_PROCESS, --package_process PACKAGE_PROCESS
                            The given <PROCESS> argument may be either a process
                            name or pid, default is "com.sankuai.meituan"
      -t1 PEFORMANCE_INTERAL, --peformance_interal PEFORMANCE_INTERAL
                            the interal of memory to get
      -t2 HROF_INTERAL, --hrof_interal HROF_INTERAL
                            the interal of hrof to ge
'''###字段说明
device_id: 是手机的device id, 通过adb 查询
save_path：保存的目录，默认是当前目录
package_process： 运行的程序
peformance_interal: 是读取的memory 间隔时间， 默认是5s
hrof_interal：是拉取内存快照的间隔时间，默认是10分钟###2. 代码测试
开始监听p = PerformanceManger()
p.begin(device_id='HT7131700092', save_path='',package_process='', peformance_interal=5, hrof_interal=300)结束监听p.end()###3. 测试场景新开发或者改进的某项功能，随着测试功能的反复使用长时间运行的稳定性测试反馈切换页面，有数据缓存的###4. 测试重点多张图片的界面，大图片网络传输大量的数据后台异步给前端###5. 查看结果默认保存在”results/device_id/日期_时间“文件下
或者保存路径“save_path/device_id/日期_时间“下。查看Dalvik和 Native 内存值的内存值曲线,内存变化的波动情况###6. 确定问题一直在增长大幅度波动
使用 Memory Analyzer Tool (MAT)分析问题##附录
####代码流程