Saravanan666/pypi-org huggingface dataset:
Package: dsQtCommon
dsLibQtCommon介绍量化公共库软件架构软件架构说明安装教程xxxxxxxxxxxx使用说明xxxxxxxxxxxx参与贡献Fork 本仓库新建 Feat_xxx 分支提交代码新建 Pull Request码云特技使用 Readme_XXX.md 来支持不同的语言，例如 Readme_en.md, Readme_zh.md码云官方博客blog.gitee.com你可以https://gitee.com/explore这个地址来了解码云上的优秀开源项目GVP全称是码云最有价值开源项目，是码云综合评定出的优秀开源项目码云官方提供的使用手册https://gitee.com/help码云封面人物是一档用来展示码云会员风采的栏目https://gitee.com/gitee-stars/
Package: dsr
IntroductionI opened this project to show datastructures by hacking__repr__function of Python.Installationpip install dsrExamplefrom dsr import BinaryTreeNode, TreeNodeprint('BinaryTreeNode:')a=BinaryTreeNode(100)b=BinaryTreeNode(2)c=BinaryTreeNode(0,a,b)d=BinaryTreeNode('a',c,c)a.right=da.left=dprint(d)print('TreeNode:')root=TreeNode('tree',[TreeNode('types',[TreeNode(str),TreeNode(int)]),TreeNode('values',[TreeNode(1),TreeNode(3.1415926),TreeNode(True)]),TreeNode('empty'),2.718281828,'Not TreeNode'])print(root)BinaryTreeNode('a')                      

                     /     \                     

              (0)                         (0)    

             /   \                       /   \   

    (100)         (2)           (100)         (2)

   /     \                     /     \           

...       ...               ...       ...TreeNode('tree')

 ├── ('types')

 │    ├── (<class 'str'>)

 │    └── (<class 'int'>)

 ├── ('values')

 │    ├── (1)

 │    ├── (3.1415926)

 │    └── (True)

 ├── ('empty')

 ├── 2.718281828

 └── 'Not TreeNode'
Package: dsrecords
Dead Simple RecordsEasy to hack and dead simple RecordIO-like data storing. Inspired by MXNet's RecordIO and Protobuf.Documentations(planned) featuresBinary-based data format with index fileEasy custom serialization schemesCommon serialization schemes (more TBA)DocumentationAppend, update, delete data samplePack/unpack formSchema serializationQuick startSee the overview section in thedocumentations.Also, check outthis notebookfor a quick example, this notebook has not been updated in a while though.How does it work?SavingOpen an empty file, this is the data file.Take whatever data you got, serialize it in a way (for example, write to PNG buffer)Write the serialized data to the said file buffer, store the offset and size of the dataRepeat until all data are writtenWrite all the offsets and sizes to another file (index file)LoadingLoad the data file and the index file.When given an index, use the index file to get the offset and size of the data.Seek the file pointer to the offset, read the exact size and deserialize the data.Prebuilt formatCustomized serialization format
Package: ds-reify
ds-reifyA CLI helper tool forDistributed-SomethingDevelopmentSetup Virtual EnvironmentSetup a python environment using the method of your choice.Using the builtinvenv:python -m venv <ENV_NAME>
source <ENV_NAME>/bin/activateUsingconda(replace with anypython >= 3.8):conda create -n <ENV_NAME> python=3.8
conda activate <ENV_NAME>Using whatever else you want, likepyenv.Install dev toolsInstallPoetrycurl -sSL https://install.python-poetry.org/ | python
source ~/.poetry/envInstallNoxpip install --user --upgrade noxSeethis postif you're curious as to why we don't install nox via Poetry.Installpre-commitpip install --user --upgrade pre-commitLet Poetry install the rest frompyproject.tomlpoetry installTestingCoverage.pyis used for test coverage, alongsidepytest, via thepytest-covplugin.To run the tests directly, in you virtual environment, runpytest --cov.To let nox run across multiple isolated environments, runnox.To avoid nox recreating the virtual environments from scratch on each invocation, runnox -r.Run a specific test withnox -s tests -- tests/test_TESTNAME.Static analysisAutoformatting is performed withBlack.Run formatting withnox -s blackor specify files/directors withnox -s black -- file1 dir1 ....Black auto-formatting is not run by default when runningnoxin isolation, it must be specified.Flake8is used for linting. Under the hood, it uses:pylintpyflakes- invalid python codeerrors reported asFpycodestyle-PEP 8style checkingWfor warnings,Efor errorsmccabe- code complexityerrors reported asC.flake8-blackplugin - adherence to Black code styleerros reported asBLK.flake8-import-orderplugin - import grouping and ordering checked against theGoogle styleguideerrors reported asIflake8-bugbearplugin - various miscellaneous bugs and design problemslikely bugs reported asBopinionated bugs reported asB9B950replacesE501for max line length checking (adds tolerance margin of 10%)flake8-banditplugin - usesBanditto find common security issuesissues reported asSflake8-annotationsplugin - detects absence of type annotations for functionsissues reported asANNAll of these are configured in the.flake8file.Run linting withnox -s lintor specify files/directoriess withnox -s lint -- file1 dir1 ....Import ordering is not auto-formatted although may in the future by migrating toflake8-isort.Safetyis uesd for checking project dependencies against known security violations. For example,insecure-package.Run it withnox -s safety.Pre-commitIf you would like to enable the pre-commit hooks, runpre-commit install.The hooks will run on files changed by the commit in question. To trigger hooks automatically runpre-commit run --all-files.
Package: dsrf-finder
DSRF Finder : An web scraperDescriptionThis library intends to make parsing HTML (e.g. scraping the web) as simple, intuitive and make the scrape as fast as possible.To installdsrf-finderlibrary in your system please follow the steps:InstallationSimply install using this commandpipinstalldsrf-finderInstall git-clonepipinstallgit-cloneClone dsrf-findergitclonehttps://github.com/thedsrf/dsrf-finder.gitGo to the project directorycddsrf_finderInstall dependenciespipinstall-rrequirements.txtIf anything gose wrong in installation please contact indsrf.anas@gmail.comDocomentationCSS Selector,xpath selection,dsrf finderVersion of Libraryversion: 0.0.4_5Based onPython version: 3.6or higher.N.B:Py versionv.3.9is recomanded.LicenseDeveloperDeveloped by:Anas bin hasan bhuiyan© dsrf.firm.pvt.ltd
Package: dsr-model-sdk
DataSpire Model SDKSupport AI Engineer an easy way to send the event of AI Model to Kafka topicQuick Starts$pipinstalldsr-model-sdk---> 100%ExampleCreate a filemain.pywith:dsr_sdk=DataSpireSDK(id='model-id-1',name='model-name-1',health_worker=True,target="kafka-bridge.local")@app.post("/predict")defprocessing(request:Request):# Start requestsess=dsr_sdk.newSession()sess.start(req:request)result={"Result":"result"}# End requestsess.completed(req:request,data=result)sess.close()returnresultCan also send error and processing:# Just created one timedsr_sdk=DataSpireSDK(id='model-id-1',name='model-name-1',health_worker=True,target="kafka-bride.local")@app.post("/predict")defprocessing(request:Request):# Start requestsess=dsr_sdk.newSession()sess.start(request)result=Nonetry:sess.processing(req:request,data={"current":1,"total":10000})sess.processing(req:request,data={"current":100,"total":10000})sess.processing(req:request,data={"current":1000,"total":10000})sess.processing(req:request,data={"current":9999,"total":10000})result={"Result":"result"}# End requestexceptException:sess.failed(req:request,error={"Exception Error":"Data input format validated failed..."})finally:sess.close()returnresult
Package: dsr-shelx
DSRThe program DSR consists of a text database with fragments of molecules and the DSR program.
It acts as a preprocessor for SHELXL .res files. The user inserts a special command in the SHELXL .res file
and the DSR program reads this information to put a molecule or fragment with the desired atoms on the position
of the target atoms or q-peaks in the unit cell. Bond restraints can be either applied from the database to the molecule
or automatically generated.The homepageI apologize for the messy code. This was my first bigger project...You have either a command line version:C:\Users\daniel>dsr
----------------------------------------------------- D S R - v238 -------------------------------------
Disordered Structure Refinement (DSR)

Example DSR .res file command line:
REM DSR PUT/REPLACE "Fragment" WITH C1 C2 C3 ON Q1 Q2 Q3 PART 1 OCC -21 =
  RESI DFIX
---------------------------------------------------------------------------------------------------------
   PUT:     Just put the fragment source atoms here.
   REPLACE: Replace atoms of PART 0 in 1.3 A distance around target atoms.
---------------------------------------------------------------------------------------------------------

optional arguments:
  -h, --help            show this help message and exit
  -r "res file" ["res file" ...]
                        res file with DSR command
  -re "res file" ["res file" ...]
                        res file with DSR command (write restraints to external file)
  -e "fragment"         export fragment as .res/.png file
  -c "fragment"         export fragment to clipboard
  -t                    inverts the current fragment
  -i "tgz file"         import a fragment from GRADE (needs .tgz file)
  -l                    list names of all database entries
  -s "string"           search the database for a name
  -g                    keep group rigid (no restraints)
  -u                    Update DSR to the most current version
  -n                    do not refine after fragment transferOr a graphical user interface inShelXle:
Package: dsrtp
Simple front-end for decrypting capturedSRTP and SRTCPpackets using:libsrtpanddpktinstallInstall devellibpcapandlibsrtpif you need to, e.g.:sudoapt-getinstalllibpcap-devlibsrtp0-devand then:pipinstalldsrtpusagecommandTo e.g. decrypt captured packets and write then back to a capture file:dsrtptest/fixtures/av.pcap/tmp/rtp.pcap-ld-ktest/fixtures/av_material.hexIf you have a cluttered capture (e.g. multiple SRTP streams) then you can
select e.g. the in-bound stream byaddress:portlike:dsrtptest/fixtures/av.pcap/tmp/rtp.pcap-ktest/fixtures/av_material.hex-ld-i192.168.121.234:60401libTo do the same in code:importdsrtpenc_km=open('test/fixtures/av_material.hex').read()km=dsrtp.KeyingMaterial.unpack_hex(enc_km)p=dsrtp.SRTPPolicy(ssrc_type=dsrtp.SRTPPolicy.SSRC_ANY_INBOUND,key=km.local)withdsrtp.SRTP(p)asctx,\open('test/fixtures/av.pcap','rb')assrtp_pcap,\open('/tmp/rtp.pcap','wb')asrtp_pcap:pkts=dsrtp.read_packets(srtp_pcap)decrypted_pkts=dsrtp.decrypt_packets(ctx,pkts)dsrtp.write_packets(rtp_pcap,decrypted_pkts)devCreate avenv:mkvirtualenvdsrtppipinstallCythonthen get it:gitclonegit@github.com:mayfieldrobotics/dsrtp.gitcddsrtpworkondsrtppipinstall-e.[test]and test it:py.testtest/--covdsrtp--cov-reportterm-missing--pep8releaseTests pass:py.testtest/--covdsrtp--cov-reportterm-missing--pep8so update__version__in:dsrtp/__init__.pycommit and tag it:gitcommit-am"release v{version}"gittag-av{version}-m"release v{version}"gitpush--tagsandtraviswill publish it topypi.
Package: dsrw
# dsrwDead simple restful wsgi applications# Installation```pip install dsrw```# Usage```pythonfrom wsgiref.simple_server import make_serverfrom dsrw import App, Requestapp = App()api = app.ns('/api')@app.get('/hello')def hello(req: Request):return {'msg': "Hello world!"}@app.get('/hello/:name')def other(req: Request):return {'msg': 'Hello','name': req.param.name}@api.get('/echo')def echo(req: Request):if req.has_valid_body:return req.bodyelse:return ["No body available"]if __name__ == '__main__':httpd = make_server('', 8080, app)print("Serving HTTP on port 8080...")# Respond to requests until process is killedhttpd.serve_forever()```# Todo- Middleware
Package: dss
#Defense Support System###UI- python dss.py start- python dss.py parse###GUI- python dss_gui.py###At this point of the project, the program has been tested in:- (32-bit and 64-bit) Windows 7 and Windows 10- (32-bit) Kali Linux----------------Linux Environment----------------You will need to install autopy. The easiest method thus far has been to run the following commands.```Shell- $ sudo apt-get install libxtst-dev- $ sudo pip install autopy```----------------Windows Environment----------------The following installations are needed to run the system on a Windows machine.###PYGTKSystem uses version: 2.24- http://ftp.gnome.org/pub/GNOME/binaries/win32/pygtk/2.24/The specific link for this version is provided below:pygtk-all-in-one-2.24.2.win32-py2.7.msi     2012-02-09 21:48   32MDirect Link- http://ftp.gnome.org/pub/GNOME/binaries/win32/pygtk/2.24/pygtk-all-in-one-2.24.2.win32-py2.7.msi###AutoPySystem uses version: 0.51- https://pypi.python.org/pypi/autopy/Plugins-----------The following installations are needed to run the plugins on a Windows machichine.###Keylogger- PIL http://www.pythonware.com/products/pil/- ConfigObj http://www.voidspace.org.uk/python/configobj.html- pyHook https://sourceforge.net/projects/pyhook/- PyWin32 https://sourceforge.net/projects/pywin32/files/pywin32/Build%20220/###Tshark- https://www.wireshark.org/download.html###Nmap- https://nmap.org/download.html#windowsAfter installing tShark and nmap from the links below follow the remaining steps:1. In the Windows command prompt type: `tshark -D`2. Copy the long string between brackets {} for "Wi-Fi" or "Ethernet"3. In tShark's run.bat file, place between the brackets for \Device\NPF_{<enter here>}4. In netscanner's config.json file enter all of the data listed into the file.(Step 1 & 2) (Example, all machines will differ in output)```ShellC:\Users\johnDoe>tshark -D1. \Device\NPF_{12345...} (VMware Network Adapter VMnet1)2. \Device\NPF_{0123456789-E123-A12 (Wi-Fi)3. \\.\USBPcap1 (USBPcap1)```(Step 3) tshark - run.bat```Batchecho offset output=%1for /f "tokens=2 delims==" %%a in ('wmic OS Get localdatetime /value') do set "dt=%%a"set "YY=%dt:~2,2%" & set "YYYY=%dt:~0,4%" & set "MM=%dt:~4,2%" & set "DD=%dt:~6,2%"set "HH=%dt:~8,2%" & set "Min=%dt:~10,2%" & set "Sec=%dt:~12,2%"set "datestamp=%YYYY%%MM%%DD%" & set "timestamp=%HH%%Min%%Sec%""C:\Program Files (x86)\Wireshark\tshark.exe" -i \Device\NPF_{123456789-1234-1234-1234-123456789} -w %output%\%datestamp%%timestamp%.pcap > NULecho on```(Step 4) netscanner - config.json```JSON{"name": "netscanner","type": "multi","enabled": false,"parser": "plugins.netscanner.parser","output": "raw","arguments": [["\\Device\\NPF_{123456789-1234-1234-1234-12345}", "VMware Network Adapter VMnet1"],["\\Device\\NPF_{123456789-1234-1234-1234-12345}", "Wi-Fi"]]}
Package: dssdata
DSSData⚡A python micro-framework for steady-state simulation and data analysis of electrical distribution systems modeled onOpenDSS.Mode support: Static and Time-series.Why DSSData?The purpose of DSSData is to facilitate the steady-state simulation of modern electrical distribution systems, such as microgrids, smart grids, and smart cities.With DSSData you can easily make your own super new fancy operation strategies with storage or generators, probabilistic simulation, or simple impact studies of a distributed generator. See an example in ourTutorial.All you need is your base distribution system modeled in OpenDSS!!!Easy to simulateWe built the DSSData for you just write what you want in a simple function, plugin on a power flow mode, and run.You don't need anymore write a routine to run each power flow per time.Quick StartInstallationWe strongly recommend the use of virtual environments manager.pip install dssdataStatic Power Flow (Snapshot)First, comment anysolve, output command (e.g:show), or solve configurations (e.g:set mode=Snap) from your.dssfile.NOTE: AnyMonitoris needed to get the data.Supposing that you file is in the pathmaster.dss:fromdssdataimportSystemClassfromdssdata.pfmodesimportrun_static_pffromdssdata.toolsimportvoltagesdistSys=SystemClass(path="master.dss",kV=[13.8,0.230],loadmult=1.0)[voltageDataFrame]=run_static_pf(distSys,tools=[voltages.get_all])print(voltageDataFrame)Time series Power FlowFirst, comment anysolve, output command (e.g:show), or solve configurations (e.g:set mode=daily stepsize=5m time=...) from your.dssfile.NOTE: AnyMonitoris needed to get the data.NOTE: TheLoadshapemust be defined in the.dssfileSupposing that you file is in the pathmaster.dss:fromdssdataimportSystemClassfromdssdata.pfmodesimportcfg_tspf,run_tspffromdssdata.toolsimportlines,voltagesdistSys=SystemClass(path="master.dss",kV=[13.8],loadmult=1.2)cfg_tspf(distSys,step_size="5m",initial_time=(0,0))[voltageDataFrame]=run_tspf(distSys,tools=[voltages.get_all],num_steps=288)print(voltageDataFrame)DocumentationWe provide an full API documentation and examples in theDSSData Documentation.CitingIf you find DSSData useful in your work, we kindly request that you cite it as below:@software{Monteiro_felipemarkson_dssdata_v0_1_7_2022,author={Monteiro, Felipe},doi={10.5281/zenodo.6784238},license={MIT},month={6},title={{felipemarkson/dssdata: v0.1.7}},url={https://github.com/felipemarkson/dssdata},version={0.1.7},year={2022}}Help us to improve DSSDataSee ourIssuesection, ourDevelopment Guidelines, and ourCode of conduct.Contributors:JonasVil
Package: dssdk
Dataset python client1、安装：pip install dssdk

或指定版本

pip install dssdk==0.1.12、ApiClient(1)、实例创建ApiClient(token: str, host=None):

token: token字符串
host: 接口服务地址，默认：api.diigii.com示例：api = ApiClient('xxxx-xxxx-xxxx')
Package: dssh
Connect Dev SSHSimple and elegant python module that helps you to manage your ssh server based on environment. Like DEV, UAT, PRE-PROD, PROD etc.
You can customize your environments/servers config as per your requirements.InstallationUse the package managerpipto install foobar.pip install dsshUsageAdd new environmentdssh addenvOutput:┏━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Available Environments ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━┩
└────────────────────────┘
New Environment name: DEVDelete an environmentdssh dlenvOutput:┏━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Available Environments ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━┩
│ DEV                    │
└────────────────────────┘
Select an environment: DEVAdd new server to an environmentdssh addserverOutput:dssh addserver
┏━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Available Environments ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━┩
│ DEV                    │
└────────────────────────┘
Select an environment: DEV
┏━━━━━━━━━━━━━━━━━━━┓
┃ Availbale Servers ┃
┡━━━━━━━━━━━━━━━━━━━┩
└───────────────────┘
New Server name: Server1
Server username: username
Server hostname: 10.10.0.1
Custom key path(path should be absolute)(Press enter if no change):
Does this server use bastion server y/n: nConnect to a serverdssh connectOutput:┏━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Available Environments ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━┩
│ DEV                    │
│ UAT                    │
└────────────────────────┘
Select an environment: DEV
┏━━━━━━━━━━━━━━━━━━━┓
┃ Availbale Servers ┃
┡━━━━━━━━━━━━━━━━━━━┩
│ Server1           │
└───────────────────┘
Select a server: Server1
Environment:DEV Server:Server1!
Connecting to username@10.10.0.1! 💥Modify config of a serverdssh modserverOutput:dssh modserver                    
┏━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Available Environments ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━┩
│ DEV                    │
└────────────────────────┘
Select an environment: DEV
┏━━━━━━━━━━━━━━━━━━━┓
┃ Availbale Servers ┃
┡━━━━━━━━━━━━━━━━━━━┩
│ Server1           │
└───────────────────┘
Select a server: Server1
Current Username - username Hostname - 10.10.0.1
New Username(Press enter if no change): nusername
New Hostname(Press enter if no change): 10.10.0.2
New key path(path should be absolute)(Press enter if no change):
Do you want to update bastion server(y/n): n
Success! 💥Remove a serverdssh dlserverOutput:┏━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Available Environments ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━┩
│ DEV                    │
└────────────────────────┘
Select an environment: DEV
┏━━━━━━━━━━━━━━━━━━━┓
┃ Availbale Servers ┃
┡━━━━━━━━━━━━━━━━━━━┩
│ Server1           │
└───────────────────┘
Select a server: Server1
Success! 💥ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.
Package: dssm
DSSMAn industrial-grade implementation of the paper:Learning Deep Structured Semantic Models for Web Search using Clickthrough DataLatent semantic models, such as LSA, intend to map a query to its relevant documents at the semantic level where keyword-based matching often fails. DSSM project queries and documents into a common low-dimensional space where the relevance of a document given a query is readily computed as the distance between them.This model can be used as a search engine that helps people find out their desired document even with searching a query that:is abbreviation of the document words;changed the order of the words in the document;shortened words in the document;has typos;has spacing issues.InstallDSSM is dependent on PyTorch. Two ways to install DSSM:Install DSSM from Pypi:pip install dssmInstall DSSM from the Github source:git clone https://github.com/Chiang97912/dssm.git
cd dssm
python setup.py installUsageTrainfromdssm.modelimportDSSMqueries=['...']# query list, words need to be segmented in advance, and tokens should be spliced with spaces.documents=['...']# document list, words need to be segmented in advance, and tokens should be spliced with spaces.model=DSSM('dssm-model',device='cuda:0',lang='en')model.fit(queries,documents)Testfromdssm.modelimportDSSMfromsklearn.metrics.pairwiseimportcosine_similaritytext_left='...'text_right='...'model=DSSM('dssm-model',device='cpu')vectors=model.encode([text_left,text_right])score=cosine_similarity([vectors[0]],[vectors[1]])print(score)DependenciesPythonversion 3.6Numpyversion 1.19.5PyTorchversion 1.9.0
Package: dss-projectfutura
DSSv3gThis is the third version of the DSS text generator, but built in a more generic way to allow for any kind of dataset.
Lots of new features have been implemented, helping DSSv3gbecome actually useful.DSSv3gis still built using the caches architecture, but additionally includes a tokenization step that allows any kind of data to be learnt.VariantsDSS has had a progression in versions and capability. Here you can explore their branches.
DSS is an architecture for building content generation ML models using caches.DSSv1Character-by-character text generation using caches. 4 char keys, 1 char values. JSON-encoded.DSSv2Word-by-word text generation using caches. 1 word keys, 1 word values. JSON-encoded.DSSv3Word-by word text generation using caches. Variable word keys, 1 word values. JSON-encoded with brotli compression.DSSv3gThe most capable DSS variant to date, capable of text generation, music generation, code completion and (limited) chatting.General (integer tokens) version of DSS content generation using caches. Variable token keys, 1 token values. Binary encoded with brotli compression.DSSv3iCurrently highly experimental and only implemented in JavaScript for easy graphics debugging.Image generation using the DSS caches architecture. Variable-kscalar keys, pixel scalar values. Binary encoded with brotli compression.KlarmuzDSSv3gpowers Klarmuz, the open platform for AI content generation. This project is possible thanks to jDev Studios and their fantastic Klarmuz compute units.
For examples on running DSSv3gmodels, please check out theKlarmuz Platform.
Package: dssp-wsl
dssp-wslConverting PDB (Protein Data Bank) file format to DSSP file format is required for generating datasets of peptides and their secondary structures.Such a task should be permitted when using BioPython. However, on Windows, the lack of the programdsspstucks everything.Fortunately, linux distros providedsspormkdsspinstallation, and Windows Subsystem for Linux is an easy-to-get thing. We hereby provide this simple script to wrap callingdssp/mkdsspfromwsl, and you can install it via:pip install dssp-wslOther than installingdssp-wslOn Windows, you should also install WSL (see hints), and usesudo apt install dsspinside the subsystem. Then you rework BioPython.