Saravanan666/pypi-org huggingface dataset:
Package: shift-ip
:triangular_flag_on_post: SHIFT (Shared Hosting IP Finder Tool):date:20-06-2023:pushpin:Initial Version 1.0.0:computer:NajibRachid:purple_circle:ANMOONSHIFT is a penetration testing tool designed to help you identify the IP addresses of shared hosting associated with a specific domain. It checks each IP address  to determine if the domain is hosted on that IP.MotivationThe development of SHIFT was motivated by the need to automate and streamline the process of bypassing reverse proxies like Cloudflare. In shared hosting environments, multiple domains and subdomains often share the same IP address, presenting a significant challenge for penetration testers and security analysts seeking to identify the precise IP addresses associated with specific domains.Manually determining the correct IP address for a domain hosted on shared hosting platforms can be a laborious and error-prone task. It typically involves time-consuming manual checks, leading to delays in conducting security assessments and penetration testing activities.FeaturesChecks range of IP addresses.Set timeout paramter.Add request headers parameter.Show response headers.Check list of IP addresses.Check for a list of domain names.Import list of IP addresses from .txt file.Import list of  domain names From .txt file.Customize check criteria.Installpipinstallshift-ipUsageSyntaxshift-ipIPDOMAINCheck IP addresses range: between1and24.shift-ip127.0.0.1/24example.comRequest headersHostheader is automatically added. In HTTP it specifies the target server's domain or host name, enabling clients to access specific domains or subdomains hosted on servers with multiple websites.Check criteriaNamecheapshared hosting: valid IP has redirect status response code301.Releases- Initial Version 1.0.0 : 20/06/2023
    - Project Setup.
    - Theory & prove of concept.
  - [Agenda] Alpha Version 1.x.x : xx/xx/2023
    - Add timeout paramter.
    - Print found IP at the end.ContributionsContributions to SHIFT are welcome! If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request on the official GitHub repository:https://github.com/n4j1Br4ch1D/SHIFTLicenseSHIFT is open-source and released under theMIT License. Feel free to modify and distribute it as per the license terms.
Package: shiftlab-ocr
SHIFTLAB OCRSHIFT OCR is a library for handwriting text segmentation and character recognition.Get Startedpip install shiftlab_ocrDoc2TextReaderfromdoc2textperforms text detection and the following recognition.import urllib

from shiftlab_ocr.doc2text.reader import Reader


urllib.request.urlretrieve(
  'https://raw.githubusercontent.com/konverner/shiftlab_ocr/main/demo_image.png',
   'test.png')
   
reader = Reader()
result = reader.doc2text("test.png")Display recognized text:print(result[0])

Действительно ли добро сильнее зла?
Именно над этим вопросом аставля заставляет
читателей задуматься В. Тендряков.
Автор рассматривает данную пробле-
му на конкретном примере, рассказывая
историю 00 заблудившемся немце русских
солдатах, которые пожалели врала и
позволи ему остаться землянке.Display segmented crops:import matplotlib.pyplot as plt

def show_img_grid(images, N):
    n = int(N**(0.5))
    k = 0
    f, axarr = plt.subplots(n,n,figsize=(10,10))
    for i in range(n):
        for j in range(n):
            axarr[i,j].imshow(images[k].img)
            k += 1
    f.show()

show_img_grid(result[1], 48)Generator of handwritingIt generates handwriting script with random backgrounds and handwriting fonts with a given string or a list of strings saved insource.txt.Generating a random sample from a string:from shiftlab_ocr.generator.generator import Generator

g = Generator(lang='ru')
s = g.generate_from_string('Москва',min_length=4,max_length=24) # get from a string
sGenerating batch of random samples fromsource.txt:import numpy as np

# upload source.txt with one word per line
g.upload_source('source.txt')
b = g.generate_batch(12,4,13) # get batch of random samples from source.txt
fig=plt.figure(figsize=(10, 10))
rows = int(len(b)/4) + 2
columns = int(len(b)/8) + 2
for i in range(len(b)):
  fig.add_subplot(rows, columns, i+1)
  plt.imshow(np.asarray(b[i][0]))Also, seeGoogle Colab Demo
Package: shift-left-secure
Shift Left N SecureSecure your project source code before pushing commits to github/gitlab/bitbucket. Project helps Software Development team to use shift left approach to find and mitigate issues at an early stage instead of taking action once code reaches to the production.InstallationUsing PipFrom PyPipython-mpipinstall-Ushift_left_secureFrom Githubpython-mpipinstallgit+https://github.com/dmdhrumilmistry/Shift-Left-Secure.gitUsing Git Clone for DevelopmentClone repogitclonehttps://github.com/dmdhrumilmistry/Shift-Left-Secure.gitchange directorycdShift-Left-SecureInstall projectpython-mpipinstall-e.UsageAdd your ChatGPT API key to environment variableexportOPEN_API_KEY=KEYUse help to view options$python-mshift_left_secure-h
usage:shift_left_secure[-h]-dDIRECTORY[-cCOMMITS][-oOUTPUT_PATH]options:
-h,--helpshowthishelpmessageandexit-dDIRECTORY,--directoryDIRECTORYdirectoryofgitproject
-cCOMMITS,--commit_hashCOMMITSnoofcommitstobeanalyzedfromcurrentHEAD
-oOUTPUT_PATH,--outputOUTPUT_PATHoutputpathtojsonfileStart analyzing changes between commits using below cmd:python-mshift_left_secure-d'path_to_git_project'-c1-otest.json
Package: shiftpy
# shiftpyTools for running Python Apps on OpenShift Red Hat Cloud[![Build Status](https://travis-ci.org/rochacbruno/shiftpy.svg?branch=master)](https://travis-ci.org/rochacbruno/shiftpy)[![Code Health](https://landscape.io/github/rochacbruno/shiftpy/master/landscape.svg?style=flat)](https://landscape.io/github/rochacbruno/shiftpy/master)<a target="_blank" href="https://www.paypal.com/cgi-bin/webscr?cmd=_donations&amp;business=rochacbruno%40gmail%2ecom&amp;lc=BR&amp;item_name=ShiftPy&amp;no_note=0&amp;currency_code=USD&amp;bn=PP%2dDonationsBF%3abtn_donate_SM%2egif%3aNonHostedGuest"><img alt='Donate with Paypal' src='http://www.paypalobjects.com/en_US/i/btn/btn_donate_SM.gif' /></a># installation```pip install shiftpy```# Available tools- env.getvar - Get openshift env var- env.listvars - Print all openshift env vars- env.getallvars - Get all vars as a dict- wsgi_utils.envify - Wrap wsgi app in Openshift Virtualenv- **ADD YOURS, please contribute**# getvar```python> from shiftpy.env import getvar> print getvar('HOMEDIR')'app-root/w543543543543543/home/'```# listvars```python> from shiftpy.env import listvars> listvars()OPENSHIFT_HOMEDIR = 'app-root/w543543543543543/home/'OPENSHIFT_APP_NAME = 'yourappname'...```# getallvars```python> from shiftpy.env import getallvars> allvars = getallvars()> print allvars{'OPENSHIFT_FOO': 'BAR','OPENSHIFT_HOMEDIR': '/tmp','OPENSHIFT_PYTHON_DIR': '/tmp/python'}```# envifyThis will wrap your wsgi app in virtualenv to OpenShift```pythonfrom shiftpy.wsgi_utils import envifyfrom myproject import app# wsgi expects an object named 'application'application = envify(app)```then your app will be available for wsgi_mod and virtualenv is activated
Package: shift-register
shiftRegisterPython library for interacting with SN74HC164 and SN54HC164 shift registers on a Raspberry Pi.DatasheetPrerequisitesThis library relys onpigpio, bothpigpiodand thepigpiopython module must be installed on your Pi.On Raspbian:sudo apt-get update
sudo apt-get install pigpio python-pigpio python3-pigpioInstallingpip install shift-registerUsageimport pigpio
from shiftregister import ShiftRegister

pi = pigpio.pi()

a_pin = 19
b_pin = 26
clr_pin = 20
clk_pin = 21

sr = ShiftRegister(pi, a_pin, b_pin, clr_pin, clk_pin)

sr.update(0b10101010) # Register outputs 10101010

sr.clear() # Register outputs 00000000

sr.toggle(0) # Register outputs 00000001

sr.toggle(7) # Register outputs 10000001

sr.write(7, 0) # Register outputs 00000001

sr.write(4, 1) # Register outputs 00010001LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details
Package: shift-tool
shiftNot an encryption toolShift was designed to store non-critical text data in such a way that only keyholders could reconstruct a useful message. Following the cryptography community's rule of "Never roll your own encryption", I am specifically not calling this an encryption tool.
I have found that through some basic testing, with a reasonable key, breaking this encoding is not easy for the average teenage 1337 haxxor, but I cannot say anything for it's durability against an experienced code breaker. If you find a way to break this, open up an issue, or contact me via email (any pull requests that can strengthen the tool are welcome too).This tool is still under development, and may not be backwards compatible with itself in the near future.InstallationTo install this tool and library, use PIP:# Install shiftpip3installshift-tool# Run shiftshift2-hNOTE: the executable is namedshift2notshift.UsageCommandlineShift's commandline tool has two modes:# Encodeshift2/path/to/input/fileyour_key_here>output.shift# Decodeshift2-d/path/to/encoded/fileyour_key_here>output.txtLibraryYou can also integrate shift2 into your own program with the library that is automatically installed with the commandline tool.importshift2.shcryptass2c# Inputsmy_key="hello_shift"my_message="I'm shifty"# Generate shifted keykey=s2c.key2shifts(my_key)# Encode the messagedata=s2c.encode(my_message,key)# Print out the encoded messageprint(data)# To decode, just use s2c.decode instead of s2c.encodeSpeedBy using Python'scProfiletool, we can see the time required to encode and decodeexample.txt. This file contains 50 paragraphs generated bylipsum.com, using the keyewpratten:Encoding:
         117661 function calls (117578 primitive calls) in 0.065 seconds
Decoding:
         151991 function calls (151904 primitive calls) in 0.092 secondsTo run your own speed tests, use thetime.shscript.QuerksThe first interesting thing about shift, is that the key can be randomly modified during encoding. This means that the message and key could actually encode eachother (this was done in my first implementation but produced files >1GB from the word "hello")Binary data must be b64 encoded before being passed through the encoder due to python safety checking unicode data.DevelopmentDeploying to PIPTo deploy shift to PIP, use:pip3 uninstall shift-tool
./deploy.sh
pip3 install --no-cache-dir shift-toolRunning locallyThis script must be run with:python3-mshift2NOTpython3shift2
Package: shifty
A pure-python list-shifter/wrapper. Shifts/Wraps an iterable left or
right by any amount (modulo to the length of iterable). Can optionally
shift the iterable in-place. Create a shifty instance to add these
convenience methods to the default list implementation if you prefer.Why slice when you can shift ‘like-a-boss’ ?!How do I get set up?python setup.py installDependencies:sixDependencies (test):Coverage, noseHow to run tests:./runtests.shDeployment instructions:pip install shiftyContribution guidelinesI accept pull requests.What about test coverage?There is a full suite of unit-tests.Who do I talk to?Francis Horsman:francis.horsman@gmail.comExample>>> from shifty import shift_left, shifty

>>> a = [1, 2, 3, 4]
>>> b = shift_left(a, 3)
>>> b
[4, 1, 2, 3]
>>> assert a is not b

>>> b = shift_left(a, 2, in_place=True)

>> assert a is b

>>> b = shift_left(a, 5)

>>> b
[4, 1, 2, 3]
Package: shifu
[![Build Status](https://travis-ci.org/wuhaifengdhu/python-shifu.svg?branch=master)](https://travis-ci.org/wuhaifengdhu/python-shifu)
[![codecov](https://codecov.io/gh/wuhaifengdhu/python-shifu/branch/master/graph/badge.svg)](https://codecov.io/gh/wuhaifengdhu/python-shifu)
# python shifu
python shifu is a project provide support to use shifu as python module.Website:http://shifu.ml## Installation
### Dependencies
shifu requires:### User installation## Development
### Important links### Source code### Setting up a development environment### Testing### Submitting a Pull Request## Project History## Help and Support
### Documentation
### Communication
### Citation
Package: shigma
shigmaShigmais a Python package allowing for sigma (Σ) and pi (Π) notation. It is very easy to learn and works like you'd expect. You'l get the hang of it very fast.How to useSigmaEnvision yourself a sigma notation.
$$\sum_{i=1}^{10} i^2$$
is this it?
This is how you write that in raw Python:arr=[]foriinrange(1,10+1):arr.append(i**2)out=sum(arr)This is how you write that with shigma.
This is how you'd write that with shigma.out=sigma_not(1,"i**2",10)Seems simple enough, right? If you are weird, or appreciate how unicode has become the standard encoding ¯\_(ツ)_/¯. Use the unicode symbol instead!out=Σ(1,"i**2",10)Yeah, unprofessional I know.PiYou'll probably be using prodcut notation a lot too. To rewrite this:
$$\prod_{i=1}^{10} i^2$$
into Python with the shigma package, you write thisout=pi_not(1,"i**2",10)unless of course...out=Π(1,"i**2",10)
Package: shihua-atom
Atom-特征库工具介绍Atom是一种特征管理工具，以数据和算子作为基本概念，数据为基础数据用于训练特征和构建特征；算子为基于固定一个或多个数据集进行新特征生产的流程，可以是一个简单直接计算函数，也可以是一个复杂的算法模型，还可以是算法模型和直接计算相结合的组合体。atom的特色是对由数据衍生的算子进行了数据关联、统一管理，并直接提供了服务功能，使得每个算子可以直接实现在线实时计算特征，为主体算法模型服务，提高模型精度。安装Atom采用Python开发，得益于Python良好的社区环境，安装支持Pythonic风格的各种管理器。$pipinstallatom-0.1-xxxxxxxxxxxx.whl快速指南首先使用atomctl命令行工具进行工作空间设置和初始化操作。然后分别启动元数据服务和atom主服务(两个服务未支持后台开启)。以下是atomctl命令行示例：$atomctlset--workspace'D:\Workspace\JiYuan\Atom\Demo\test'$atomctlinit$atomctlmetadata-service$atomctlstart-service然后就是使用python脚本进行atom的数据和算子操作。主要包括数据和算子的注册、查询、删除三个基本操作以及算子的加载操作以下是atom主程脚本代码示例：fromatom.schedulerimport*### 加载Atom调度器atom=AtomScheduler(mode='delay')### register-data测试df=pd.read_csv(r"D:\Workspace\JiYuan\WindPowerForecast\LSTM\demo\merge_data_GDTYUAN_ec.csv")atom.data_register(tag='test',belong='first',object_name='merge_data_GDTYUAN_ec_1',data_object=df,remarks='this is a test data!')### register-operator测试### 即时模式----装饰器方式# @atom.operator_register(tag='test',#                         belong='first',#                         object_name='test_function_a',#                         remarks='this is a test operator!')deftest_function(a,b):c=a+b*2+1returnc# tmp_a = test_function(1,2)### 及时模式----函数方式# tmp_func = atom.operator_register(tag='test',#                         belong='first',#                         object_name='test_function_b',#                         remarks='this is a test operator!')(test_function)# tmp_b = tmp_func(3,4)### 延时模式atom.operator_register(tag='test',belong='first',object_name='test_function_a',## ccoperator_object=test_function,remarks='this is a test operator!')# ### data-remove测试# atom.data_remove(tag='test',object_name='merge_data_GDTYUAN_ec_00')### operator-remove测试# atom.operator_remove(tag='test',object_name='test_function_cc')### data-query测试data_view_df=atom.data_query(tag='test')print(data_view_df)### operator-query测试operator_view_df=atom.operator_query(tag='test')print(operator_view_df)### data-modify测试atom.data_modify()### operator-modify测试atom.operator_modify()### data-load测试data_load_df=atom.data_load(tag='test',object_name='merge_data_GDTYUAN_ec_1')print(data_load_df)### operator-load测试operator_load_a=atom.operator_load(tag='test',object_name='test_function_a')print(operator_load_a(10,20))# print(test_function(**{'a':10,'b':20})) ### 字典参数传递最后是算子在线计算服务的使用。当一个算子注册到atom后，他就自动获得了在线计算服务的功能。表单数据格式如下：### 该表单数据仅以python为例展示post_form={"tag":"test","object_name":"test_function_a","data_json":{"a":78,"b":9}}设计WEBUIDATAUI使用工厂模式，解耦计算、存储和通信所使用的第三方工具设计数据和算子两个基本概念，扩展了特征工程工具的适用范围，一个算子不仅可以直接计算，还可以是复杂算法模型，覆盖特征工程的特征挖掘和关键指标计算管理。算子一次注册即拥有长效计算服务功能。算子基于不同数据实现了版本管理，更具有实际意义技术列表工厂模式MinIOBootstrap5SQLite3RabbitMQFastAPI
Package: shihua-cask
Cask-存储管理工具介绍cask是一个存储管理工具，主要功能包括项目本地打包，远端存储管理两大功能，主要设计仿制wheel文件格式，远端存储采用MinIO技术。安装cask采用Python开发，得益于Python良好的社区环境，安装支持Pythonic风格的各种管理器。$ pip install cask-0.1-xxxxxxxxxxxx.whl快速指南python-sdk使用cask提供python-sdk的存储管理，以下是cask主程脚本示例：：fromcaskimportcreate_cask_workspace,generate_cask_package,install_caskpkg,seriallize,deseriallizefromcask.packageimportCaskpkgfromcask.storeimportCaskStore### 创建cask工作空间，生成.cask隐藏文件夹# create_cask_workspace()### 生成cask包# generate_cask_package(caskpkg_name='testcask')### 安装cask包，生成.cask隐藏文件夹# install_caskpkg(caskpkg_file='testcask.caskpkg')### 添加object_file到.cask下# tmp_caskpkg = Caskpkg(objects=['logtest'],scripts=['install_script.sh'],configs=['test.yaml'])# tmp_caskpkg.commit()### 初始化cask存储对象caskstore=CaskStore(endpoint='192.168.1.82:9000')### 临时函数deftestfunc(a):returna+1### 序列化对象到文件seriallize(target_object=testfunc,filepath='testfunc.dill')### 序列化对象testfunc_bytes_obj=seriallize(target_object=testfunc)### 上传二进制对象result=caskstore.push_object(bucket_name='cask',object_name='testfunc',bytes_obj=testfunc_bytes_obj)print(result)### 获取二进制对象tmp_bytes_obj=caskstore.pull_object(bucket_name='cask',object_name='testfunc')### 反序列化对象testfunc_bytes_obj_a=deseriallize(target_object=tmp_bytes_obj)print(testfunc_bytes_obj_a(1))### 上传文件result=caskstore.push_file(bucket_name='cask',object_name='testfunc.dill',file_path='testfunc.dill')print(result)### 获取文件caskstore.pull_file(bucket_name='cask',object_name='testfunc.dill',file_path='testfunc.dill')### 反序列化对象testfunc_bytes_obj_aaa=deseriallize(filepath='testfunc.dill')print(testfunc_bytes_obj_aaa(2))命令行使用cask提供命令行的存储管理，以下是cask命令行示例：$caskctlcreate-workspace$caskctlgenerate-package--caskpkg_nametestcask$caskctlinstall-package--caskpkg_filetestcask.caskpkg$caskctlupload-file--endpoint192.168.1.82:9000--bucket_namecask--object_nametest.yaml--file_pathtest.yaml$caskctldoenload-file--endpoint192.168.1.82:9000--bucket_namecask--object_nametest.yaml--file_pathtest.yaml设计仿制wheel格式设计caskpkg打包格式使用MinIO作为远端存储支持本地打包和远端存储两大功能技术列表__init__收集基础操作函数__all__暴露指定功能.cask隐藏文件夹yaml配置文件MinIO远端存储Tar压缩包技术设计UML图以下是设计的UML图：
Package: shihua-cuber
Cuber介绍cuber是一个集成学习框架，主要用于各种集成学习算法的开发，具有灵活动态可配置的特点。cuber主要由三大模块组成，控制引擎、计算引擎和调度引擎。控制引擎依托于networkx的有向无环图技术，需要以计算引擎和调度引擎为基础，运行时加载。计算引擎支持Ray,Dask；调度引擎支持Kahn算法。安装Cuber采用Python开发，得益于Python良好的社区环境，安装支持Pythonic风格的各种管理器。$pipinstallcuber-0.1-xxxxxxxxxxxx.whl快速指南cuber使用主要分为三大步，首先初始化cuber实例，加载计算引擎和调度引擎，获取cuber控制器实例；然后使用装饰器注册目标节点的函数；最后调用cuber控制器执行以下是cuber主程脚本代码示例：### 载入程序包fromcuber.interfaceimportCubeimporttime### cuber实例创建cuber=Cube(cuber_runner='ray',cuber_runner_address='ray://192.168.1.51:10001',cuber_scheduler='kahn')### 创建cuber控制器cuber_controller=cuber.get_cuber_controller()### 注册目标函数到指定cuber控制器@cuber_controller.register(controller_obj=cuber_controller)deftest_a():time.sleep(10)print(2)return'a'@cuber_controller.register(controller_obj=cuber_controller)deftest_aa():time.sleep(10)print(2)return'aa'@cuber_controller.register(controller_obj=cuber_controller)deftest_b():time.sleep(10)print(2)return'b'@cuber_controller.register(controller_obj=cuber_controller)deftest_bb():time.sleep(10)print(2)return'bb'@cuber_controller.register(controller_obj=cuber_controller)deftest_c():time.sleep(10)print(2)return'c'@cuber_controller.register(controller_obj=cuber_controller)deftest_cc():time.sleep(10)print(2)return'cc'@cuber_controller.register(controller_obj=cuber_controller)deftest_d():time.sleep(10)return'd'### 注册目标函数依赖关系到指定cuber控制器test_a>>test_b>>test_c>>test_dtest_aa>>test_b>>test_dtest_bb>>test_c>>test_dtest_cc>>test_d### 展示节点和边情况print('~~~~~~',cuber_controller.get_graph_obj())print('------',cuber_controller.show_nodes())print('======',cuber_controller.show_edges())### 使用二级统一APItime_start=time.time()exec_result=cuber_controller.execute()time_end=time.time()print('============================== Parallel function running',time_end-time_start)### 串行函数运行time_start=time.time()test_a()test_aa()test_bb()test_cc()test_b()test_c()test_d()time_end=time.time()print('============================== Serial function operation',time_end-time_start)设计大量使用元编程技术，提高代码灵活性、可读性和维护质量设计了控制引擎、计算引擎和调度引擎，实现灵活动态可扩展天然支持分布式三级API设计，使用方便，适应不同程度的使用人员技术列表__new__技术__init__技术__prepare__技术__call__技术type元类描述符技术Mixin装饰器技术namedtupleNetworkxFSM-transitions运算符重载__slots__技术Ray
Package: shihua-dashare
DaShare-数据服务接口包介绍DaShare是一个数据服务接口包，主要功能是为底层数据向外提供了一个接口服务,可实现用户权限验证和流量限制。DaShare采用Client-Server架构设计，服务端使用HTTP协议构建数据服务的OpenAPI,暂时采用GET-method,客户端封装为python-sdk,提供python数据接口功能；用户权限采用token机制。安装dashare采用Python开发，得益于Python良好的社区环境，安装支持Pythonic风格的各种管理器。$ pip install dashare-0.1-xxxxxxxxxxxx.whl快速指南服务端使用对于服务端数据服务启动，首先使用dashare命令行进行元数据库初始化,然后再启动服务端服务。以下是dasharectl命令行示例：$dasharectlset-metadb$dasharectlstart-service客户端使用对于客户端，dashare支持http协议的接口和python-sdk两种方式。http接口使用
1.注册用户示例$curlhttp://127.0.0.1:11911/user/register?user=test&password=1234562.登录用户示例$curlhttp://127.0.0.1:11911/user/login?user=test&password=1234563.用户生成token示例$curlhttp://127.0.0.1:11911/user/gen_token?user=test&token_key=78904.用户获取token示例$http://127.0.0.1:11911/user/get_token?user=test&password=1234565.用户调用数据接口示例（用户调用数据接口需要经过前提流程，注册用户-登录用户-生成token-使用token获取数据）$http://127.0.0.1:11911/meteorological/get_wind_nwp_data_updated?token_key=7890&token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJleHAiOjE2NjU1Mzg0MzkuMTU3MTQzLCJ1c2VyIjoidGVzdCJ9.ZXdp688IIJgnFgSqgRx7rXBxYNK0LLDZTuQL_P09saE&entity=XJJX&start_time=2022-05-30,00:00:00&end_time=2022-06-01,00:00:00python-sdk使用,以下是dashare主程脚本代码示例：fromdashare.client.apiimportDataAPI### 设置除了token_key和token外，额外的参数tmp_dict={}tmp_dict['entity']='XJJX'tmp_dict['start_time']='2022-05-30,00:00:00'tmp_dict['end_time']='2022-06-01,00:00:00'dashare=DataAPI(token_key=7890,token='eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJleHAiOjE2NjU0NjkxMTQuMTI4MDg4LCJ1c2VyIjoidGVzdCJ9.HgmyjDabinqOxU9v7DaKsPraFZd948SMfA9HGhTg-7U',timeout=6000)# df = dashare.query(dataapi='get_meteorological_data_local',params=tmp_dict)# df = dashare.get_wind_nwp_data_updated_local(params=tmp_dict)# df = dashare.get_wind_nwp_data_history_local(params=tmp_dict)# df = dashare.get_wind_measure_data_local(params=tmp_dict)df=dashare.get_wind_turbine_data_local(params=tmp_dict)print(df)设计采用Client-Server架构设计服务端使用HTTP协议构建数据服务的OpenAPI用户权限采用token机制用户流量限制技术列表元编程技术-partial技术元编程技术-__getattr__技术微服务-FastAPI加密验证-Token-JWT技术数据库-SQLite3设计UML图以下是设计的UML图：
Package: shihua-drip
Drip-算法全生命周期管理工具介绍Drip介绍Drip设计说明Drip是一个轻量的生产级算法平台，覆盖从算法实验、算法运行前管理、算法运行时管理到算法运行后管理的全生命周期。Drip的核心概念包括信息管理、存储管理、日志管理、调度管理、特征管理、数据通道和插件管理。Drip每个核心功能都由独立模块支持，完全可单独使用。Drip设计架构图逻辑视图物理视图处理视图开发视图安装$pipinstalldrip-0.1-xxxxxxxxxxxx.whl快速指南首先使用dripctl命令行工具进行管理员注册操作，然后启动webui后台服务。另外，后端详细配置在drip_config.yaml中。以下是dripctl命令行示例：$dripctlcreate-admin

$dripctlstart-webuiDrip算法研发流程以下为DripWebUI使用界面设计Drip核心模块介绍信息管理：Fiche设计说明Fiche是一个元数据信息管理工具，主要功能提供元数据信息的管理，包括算法信息、模型信息、参数信息、应用信息和数据信息五大类信息；主要设计采用将信息卡片化，存储为一个个json字符串文档，采用主从推送模型实现观察者模式，便于信息中心化；主要技术采用MongoDB作为后端信息数据的中心存储，基于FastAPI实现的http服务使得该工具提供python-sdk的同时也与语言无关。设计架构图存储管理：Cask设计说明cask是一个存储管理工具，主要功能包括项目本地打包，远端存储管理两大功能，主要设计仿制wheel文件格式，远端存储采用MinIO技术。设计架构图调度管理：Liquid设计说明liquid是一个pipeline工具，主要功能提供算法应用快捷组织功能，主要技术使用基于pluggy的hook技术。设计架构图日志管理：Trajectory设计说明trajectory是一个日志管理工具，主要功能提供日志统一集中管理功能，主要技术基于logging提供一个mongodb的日志转发器，包括日志过滤、日志格式化功能。设计架构图数据通道：DaShare设计说明DaShare是一个数据服务接口包，主要功能是为底层数据向外提供了一个接口服务,可实现用户权限验证和流量限制。DaShare采用Client-Server架构设计，服务端使用HTTP协议构建数据服务的OpenAPI,暂时采用GET-method,客户端封装为python-sdk,提供python数据接口功能；用户权限采用token机制。设计架构图特征管理：Atom设计说明Atom是一种特征管理工具，以数据和算子作为基本概念，数据为基础数据用于训练特征和构建特征；算子为基于固定一个或多个数据集进行新特征生产的流程，可以是一个简单直接计算函数，也可以是一个复杂的算法模型，还可以是算法模型和直接计算相结合的组合体。atom的特色是对由数据衍生的算子进行了数据关联、统一管理，并直接提供了服务功能，使得每个算子可以直接实现在线实时计算特征，为主体算法模型服务，提高模型精度。设计架构图