Saravanan666/pypi-org huggingface dataset:
Package: knitty-gritty
Dropbox for knitting machinesKnitty-gritty manages your knitting machine patterns. You place all your
pattern images a folder, and this folder will be uploaded to the machine each
time you tell the machine to import patterns. You can of course pre-populate
the folder with patterns downloaded from the machine.FeaturesReads or writes BMP, PNG, and JPEG images.Compacts the memory used to avoid gaps (fragmentation!) in memory, allowing
you to use (almost) 100% of your machine’s 32 kb memory.What Doesn’t Work?Adding data to the memo display.Validating that the pattern fits within the machine’s working memory.Platform SupportOnly tested on OS X, but should work out of the box on both Windows and Linux
given that you have the software requirements listed below installed. Please
let me know if it does not - preferably with a pull request fixing the issue
:-)What You Will NeedHardware:Brother KH940 knitting machine. KH930mightwork but is untested.USB FTDI cable connected to the machine.Software:A Python (2.7) installation.Preferably a Python virtual environment.Installation InstructionsKnitty-gritty is distributed as a Python package. Install it via pip:pipinstallknitty-grittyIf that completed successfully, you will now have aknitty-grittyexecutable, ready to do its thing!Downloading Patterns from the Machine# First, find your USB cable:ls/dev/tty.usbserial-*# Replace the file below with the name you found
# The last parameter `patterns` is the folder where your patterns should be savedknitty-grittyemulate-folder/dev/tty.usbserial-A7XTW5YZpatternsIf this is the first time you run knitty-gritty, you should download all
patterns from the machine first. On a KH940, this is done by enteringCE,552,STEP,1,STEP. When this is done, the machine should beep
(as it always does). Quit Knitty-gritty by pressing Control-C. The pattern
images should appear in the folder you specified.Now you can modify/add/remove patterns as much as you like. Just drop them in
the folder together with the other patterns.Uploading PatternsWhen you’re done with fiddling with the images, you should upload them:# Replace the TTY-parameter with the cable you found above.knitty-grittyemulate-folder--no-save/dev/tty.usbserial-A7XTW5YZpatternsThe--no-saveargument tells Knitty-gritty to not save any files to the
folder on the computer. This is a safeguard against removing/overwriting
patterns if anything goes wrong.To load the patterns on the machine, enterCE,551,STEP,1,STEPand wait until it beeps.AcknowledgementsThe file format/memory dump file format documentation over at STG’sknittingtonrepository was a huge help in writing the parser/serializer.Steve Conklin’s PDDemulate.py inknitting_machinewas very useful in
filling the gaps in Tandy’s official floppy drive command documentation.
Package: knlm
knlmModified Kneser-Ney smoothing language model module for PythonInstallation$ pip install knlm
$ pip3 install knlmExamplefrom knlm import KneserNey

mode = 'build'
if mode == 'build':
    # build model from corpus text. order = 3, word size = 4 byte
    mdl = KneserNey(3, 4)
    for line in open('corpus.txt', encoding='utf-8'):
        mdl.train(line.lower().strip().split())
    mdl.optimize()
    mdl.save('language.model')
else:
    # load model from binary file
    mdl = KneserNey.load('language.model')
    print('Loaded')
print('Order: %d, Vocab Size: %d, Vocab Width: %d' % (mdl.order, mdl.vocabs, mdl._wsize))

# evaluate sentence score
print(mdl.evaluateSent('I love kiwi .'.split()))
print(mdl.evaluateSent('ego kiwi amo .'.split()))

# evaluate scores for each word
print(mdl.evaluateEachWord('I love kiwi .'.split()))
print(mdl.evaluateEachWord('ego kiwi amo .'.split()))
Package: knlp
knlp这是一个工具包，主要实现对中文的NLP基础操作，本pkg的主体架构参考了snownlp和textblob，因为个人认为这种实现方式对于调用方来说最方便。这个pkg提供各种能力，也提供基础算法的训练和推理的脚本，还提供了很多现成的对各种nlp任务的评估方法以及相应的评估数据集（或者地址），提供深度学习并且面向中文开发，且功能很基础，适合于based on这个进行二次改造。欢迎提出issue或者私信交流安装方式pip install knlp

# FROM GITHUB SOURCE CODE
pip install git+https://github.com/DukeEnglish/knlp.git示例方法fromknlpimportKnlpdeftest_all():withopen("knlp/data/pytest_data.txt")asf:text=f.read()res=Knlp(text)print("seg_result is",res.seg_result)print("ner_result is",res.ner_result)print("sentiment score is",res.sentiment)print("key_words are",res.key_words)print("key sentences are",res.key_sentences)gt_string='就读 于 中国人民大学 电视 上 的 电影 节目 项目 的 研究 角色 本人 将 会 参与 配音'pred_string='就读 于 中国 人民 大学 电视 上 的 电影 节目 项 目的 研究 角色 本人 将 会 参与 配音'print("evaluation res are",res.evaluation_segment(gt_string,pred_string))abs_path_to_gold_file=''abs_path_to_pred_file=''gt_file_name=f'{abs_path_to_gold_file}'pred_file_name=f'{abs_path_to_pred_file}'print("evaluation file res are",res.evaluation_segment_file(gt_file_name,pred_file_name))其他示例使用方法在samples中。所有的训练数据都在data中有示例数据。sample使用方法序列标注的训练1.1 首先生成训练数据，序列标注的数据处理方法在knlp/seq_labeling/data_helper.py。数据针对的是人民日报的数据。1.2 其次进行训练并使用samples/hmm_sample.py，进行hmm的训练：https://zhuanlan.zhihu.com/p/358825066信息提取（关键词、关键短语、摘要）2.1 samples/IE_sample.py参考并致谢在实现过程中，调研了网络上很多已经开源的工具包，对他们致以深深的感谢。在coding过程中，参考学习了很多参考pkg中的编码方式，也有直接调用。如果作者感觉到被冒犯，请随时私信联系。snownlpjiebatextblobhttps://www.letiantian.me/2014-06-10-pagerank/评估结果离线评估Clue榜单评估结果
Package: knmi-py
# KNMI-py
Python wrapper to fetch and parse observations from KNMI,
either as csv or Pandas DataFrameSeehttp://knmi.nl/kennis-en-datacentrum/achtergrond/data-ophalen-vanuit-een-scriptNote: This library is not affiliated in any way with KNMI, it only uses the KNMI API to request data.Currently only daily data is implemented.## Installation
KNMI-py is available via pip.python -m pip install knmi-py## 5 Functions
-knmi.get_day_data_raw(stations, start, end, inseason, variables)-knmi.get_day_data_dataframe(stations, start, end, inseason, variables)-knmi.get_hour_data_raw(stations, start, end, inseason, variables)-knmi.get_hour_data_dataframe(stations, start, end, inseason, variables)-knmi.get_forecast_dataframe(station, conform_values, variables)## Metadata
Don’t know which station number you need, or what the variables mean?
We got you covered:knmi.stationsprovides a list of all available stationsknmi.variablesprovides a list of all variables and explanations## Metadata included in DataFrame
All raw data is included as argument to the DataFrame
(however, the data is lost once you start manipulating the frame, so you’ll have to copy it)# Example`python import knmi df =knmi.get_day_data_dataframe(stations=[260])print(df.disclaimer) print(df.stations) print(df.legend) df = df.rename(columns=df.legend) print(df) `## DisclaimerThe KNMI-py Python library is not affiliated, created or maintained by KNMI. It merely uses the KNMI API to request data.
Package: knnn
knnnK-Nearest Neighbors of NeighborspipinstallknnnDescriptionThis package provides a simple implementation of the K-Nearest Neighbors of Neighbors algorithm. The algorithm is a simple extension of the K-Nearest Neighbors algorithm, which is used for anomaly detection. The algorithm is based on the idea that the neighbors of the neighbors of a point gives more information than its neighbors. The algorithm can be used to improve the accuracy of the KNN algorithm.UsagefromknnnimportKNNNimportnumpyasnp# Random datax_normal=np.random.rand(100,2)x_test=np.random.rand(20,2)+1# Create a KNNN objectknnn=KNNN(num_neighbors=3,num_neighbors_of_neighbors=25)# Fit the modelknnn.fit(x_normal)# Predict the labels of the test datay_pred=knnn.predict(x_test)InstallationThe simplest way to install the package is to run:pipinstallknnnIf you want to install the latest version from the master branch:(-e option will allow you to change the code without reinstalling the package)gitclonehttps:\\github.com\knnncdknnn
python3-mpipinstall-e.If you want to build the package from source, run:python3-mbuildand to install the built package, run:python3-mpipinstall--force-reinstalldist/*.whlTo run the tests, run:pytest
Package: kNNp
kNN-P Classifier (Under Development)OverviewThis repository contains an implementation of the kNN-P classifier, an enhanced version of the k-nearest neighbors algorithm utilizing membrane computing. kNN-P is designed for parallel and distributed computing, which can improve the performance of the original k-nearest neighbors algorithm for classification tasks.Please note that this project is currently under development.FeaturesImplementation of the kNN-P classifier.Designed for parallel and distributed computing.Improved performance for classification tasks.InstallationYou can install this package using pip:pipinstallknnpUsagefromknnp.p_systemsimportkNN_P# Create an instance of kNN-Pknn_p=kNN_P(n=100,d=2,q=10,m=5,k=3,maxstep=100)# Load your training data (features) and class labelstraining_data=...class_labels=...# Train the classifierknn_p.fit(training_data,class_labels)# Load your test datatest_data=...# Make predictionspredictions=knn_p.predict(test_data)# Evaluate the predictions and calculate classification metrics...ContributingContributions to this project are welcome. Please follow these guidelines for contributing:Fork the repository.Create a new branch for your feature or bug fix.Commit your changes.Push your branch to your fork.Create a pull request with a clear description of your changes.LicenseThis project is open-source and available under theMIT License.ContactIf you have questions or need further assistance, please feel free to reach out toKhushiyant.Please note that this project is under development. Use it with caution, and contributions are encouraged.
Package: knobs
>>>pirates=Knob('JOLLY_ROGER_PIRATES',124,description='Yar')>>>pirates.get()124>>>pirates.get_type()>>><type'int'>Useknobsif you worship at the church of the12 Factor AppA knob is a wrapper for an environment variable. It can:Read and write an environment variableMake sure it is of the expected typeValidate a value is good.knobswill search for a nominated environmental file (default.env) and load that
into the environment. A knob is type aware, configured from the environment and its value can be
persisted to ease the creation of configuration files.InstallInstall from pypi$ pip install knobsInstall from source$ pip install .
Package: knockapi
Knock Python libraryKnock API access for applications written in Python.DocumentationSee thedocumentationfor Python usage examples.InstallationTo install from PyPi, run the following:pipinstallknockapiTo install from source, clone the repo and run the following:pythonsetup.pyinstallConfigurationTo use the library you must provide a secret API key, provided in the Knock dashboard.fromknockapiimportKnockclient=Knock(api_key="sk_12345")UsageIdentifying usersfromknockapiimportKnockclient=Knock(api_key="sk_12345")client.users.identify(id="jhammond",data={"name":"John Hammond","email":"jhammond@ingen.net"})Retrieving usersfromknockapiimportKnockclient=Knock(api_key="sk_12345")client.users.get_user(id="jhammond")Sending notifiesfromknockapiimportKnockclient=Knock(api_key="sk_12345")client.notify(key="dinosaurs-loose",actor="dnedry",recipients=["jhammond","agrant","imalcolm","esattler"],cancellation_key=alert.id,tenant="jurassic-park",data={"type":"trex","priority":1})Getting and setting channel datafromknockapiimportKnockclient=Knock(api_key="sk_12345")# Set channel data for an APNSclient.users.set_channel_data(id="jhammond",channel_id=KNOCK_APNS_CHANNEL_ID,channel_data={"tokens":[apns_token]})# Get channel data for the APNS channelclient.users.get_channel_data(id="jhammond",channel_id=KNOCK_APNS_CHANNEL_ID)Canceling notifiesfromknockapiimportKnockclient=Knock(api_key="sk_12345")client.workflows.cancel(key="dinosaurs-loose",cancellation_key=alert.id,recipients=["jhammond","agrant","imalcolm","esattler"],)User preferencesfromknockapiimportKnockclient=Knock(api_key="sk_12345")# Replaces the preferences for the userclient.users.set_preferences(user_id="jhammond",channel_types={'email':True},workflows={'dinosaurs-loose':False})# Retrieve the current preferencesclient.users.get_preferences(user_id="jhammond")Signing JWTsYou can use thepyjwtpackage tosign JWTs easily.
You will need to generate an environment specific signing key, which you can find in the Knock dashboard.If you're using a signing token you will need to pass this to your client to perform authentication.
You can read more aboutclient-side authentication here.importjwtimportosprivate_key=os.getenv("KNOCK_SIGNING_KEY")encoded=jwt.encode({"sub":"jhammond"},private_key,algorithm="RS256")
Package: knocker
# knock... knock...![](https://media.giphy.com/media/4WONcbcxCOYRa/giphy.gif)Yet another port knock client.#### Installing`$ pip install knocker`#### Running```bash$ knocker your.server.domain 8001 8002 8003knock... knock...```##### Getting help```$ knocker -husage: knocker [-h] [-d DELAY] host ports [ports ...]Knock host and port using tcp connectionpositional arguments:host                  Hostname or IP address of the hostports                 Ports to knockoptional arguments:-h, --help            show this help message and exit-d DELAY, --delay DELAYMilliseconds between each knock```#### Example serverThe `example_server.py` is just a socket server that listen 3 differentports and print a message after receive a ping on it.```$ python example_server.pyListening ports: 49485 38749 42846```
Package: knockoff
Knockoff FactoryA library for generating mock data and creating database fixtures that can be used for unit testing.Table of contentInstallationChangelogDocumentationUnit TestsFuture WorkLegacy YAML Based CLIInstallationFrom PyPi:pipinstallknockoff# to install with PyMySQLpipinstallknockoff[mysql]# Note: Other MySql DBAPI's can be used if installed and dialect provided in connection urlFrom GitHub:pipinstallgit+https://github.com/Nike-Inc/knockoff-factory#egg=knockoff# to install with PyMySQLpipinstallgit+https://github.com/Nike-Inc/knockoff-factory#egg=knockoff[mysql]# Note: Other MySql DBAPI's can be used if installed and dialect provided in connection urlChangelogSee thechangelogfor a history of notable changes to knockoff.DocumentationWe are working on adding more documentation and examples!Knockoff SDKKnockoffTableKnockoffDBTempDatabaseServiceKnockoff CLIUnit Testing Example: Sample AppUnit TestsPrerequisitesdockerpoetry (curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python)Some of the unit tests depend on a database instance. Knockoff will create ephemeral databases within that instance and clean
them up when tests have completed. By default it will attempt to connect to an existing
instance atpostgresql://postgres@localhost:5432/postgresand will
create and destroy databases per test. This postgres location can
be overridden with theKNOCKOFF_TEST_DB_URIenvironment variable.If no external postgres instance is available for testing, but postgresql is
installed, theTEST_USE_EXTERNAL_DBenvironment variable can be set to0.
The fixtures will then rely on thetesting.postgresqllibrary to create
ephemeral postgres instances per fixture.If postgres is not available, dependent tests can be disabled with the
following:export TEST_POSTGRES_ENABLED=0.Some tests also depend on a MySql database instance. These tests can be
disabled with the following:export TEST_MYSQL_ENABLED=0.Create the database instance using docker:# Run postgres instancedockerrun--rm--namepg-docker-ePOSTGRES_HOST_AUTH_METHOD=trust-d-p5432:5432postgres:11.9# Run mysql instancedockerrun--namemysql-docker-eMYSQL_ALLOW_EMPTY_PASSWORD=yes-p3306:3306-dmysql:8.0.26Install poetry:# the -E flag is so we can run the mysql unit tests with the PyMySql DBAPIpoetryinstall-EmysqlRun unit test:poetryrunpytestFuture workFurther documentation and examples for SDKAdd yaml based configuration for SDKMake extensible generic output for KnockffDB.insert (csv, parquet, etc)Enable append option for KnockoffDB.insertAutodiscover and populate all tables by using reflection and building dependency graph with foreign key relationshipsParallelize execution of dag. (e.g.https://ipython.org/ipython-doc/stable/parallel/dag_dependencies.html)
Package: knock-on-gpus
knock-on-gpusA CLI tool for checking if GPUs are available before running your script that uses GPUs.Installationpipinstallknock-on-gpusQuick startBasic usageYou can useknock-on-gpusto run a script that uses GPUs.knock-on-gpus--pythonmy_script.pyIf some GPUs are not available,knock-on-gpuswill return an error code and print a message to the console.Using withCUDA_VISIBLE_DEVICESYou can also useknock-on-gpusto run a script with specific GPUs.CUDA_VISIBLE_DEVICES=0,1knock-on-gpus--pythonmy_script.pyYou can also use--devicesor-dto specify the GPUs to use.knock-on-gpus-d0,1--pythonmy_script.pyAuto selectionYou can use--auto-selectto automatically allocate the number of GPUs.knock-on-gpus--auto-select2--pythonmy_script.pyIf GPU:0, GPU:1, and GPU:3 are unavailable,knock-on-gpuswill use GPU:2 and GPU:4.Set alias forpythonYou can set an alias forpythonto useknock-on-gpusby default.aliasunsafe-python="`which python`"aliaspython="knock-on-gpus -- python"Then you can run your script withoutknock-on-gpus.Options--devices(Alias:-d,--device)Specifies the GPUs to use. The value is a comma-separated list of GPU IDs.--memory-border-mibSpecifies the memory border (MiB) to treat as vacant. If the memory usage exceeds this value, the GPU will be treated as occupied.--use-gpu-strictlyIf true, use GPU strictly. If CUDA is not available, it will fail.--min-gpusSpecifies the number of min GPUs to use.--max-gpusSpecifies the number of max GPUs to use.--cuda-visible-devices-env-keySpecifies the environment variable key to set visible devices.--verboseIf true, print verbose logs.--auto-select(Alias:-a,--auto)If a number is given, it will automatically allocate the number of GPUs.
Package: knocky
knocky介绍knocky包用于通信时帧头和帧正文处理解耦。帧正文处理函数注册成knocky包的服务，帧头处理调用服务，调用knocky服务获取响应数据。本软件包已上传到pypi，可输入命令直接安装。pipinstallknocky示例importknockyasknockdefmain():knock.register(0,5,service1)print(knock.call(0,5,bytearray([4,5])))defservice1(req:bytearray,*args)->(bytearray,bool):print('service1 get req:',req,args)returnbytearray([1,2,3]),Trueif__name__=='__main__':main()输出:service1 get req: bytearray(b'\x04\x05') ()
resp: (bytearray(b'\x01\x02\x03'), True)
Package: knok-knok
knok_knok: Pushing Notifications Now Becomes Simple!What is it?knok_knokis an easy-to-use python package for sending push notifications across andriod and ios devices.Tired of looking around for solutions to send your notifications?No more worries now,knok_knokdoes all the heavy lifting of research and bug-fixes for you. Let's get started.Our Features includesending push notifications for the fcm tokens provided. Be it a token for an android device or an ios device, it doesn't matter.sending notifications to any number of devices at a timemultiple notifications on a single device getting collapsed on the notification bar.sending notifications that redirects the user somewhereGCM has been deprecated so if you are someone who is migrating from GCM to FCM, you can also give us a try.Getting StartedThe source code is currently hosted on GitHub at:https://github.com/ajaysharma132/knok-knokBinary installers for the latest released version are available at thePython Package Index (PyPI).# PyPIpipinstallknok-knokfor sending a push notification, all you need to do is -build a data_map containing the information for your notification messageprovide a list of fcm tokens for the devices you want to trigger a push notification# data_map{"title":"Hello","body":"World !","url":"https://www.google.com/"}title - represents the title of the your notificationbody - represents the body/text/message of your notificationurl - is for the link you want the user to redirect to..# tokens['token1','token2','token3',]Andsoon...YoucanprovideanynumberoftokensNow, instantiate the FCMClient providing your FCM_API_KEY as -# FCMClient instancefromknok_knokimportFCMClientclient=FCMClient(FCM_API_KEY)And use the send function to send your notifications as -# send notificationclient.send(data_map,tokens)you see, it's that simple!!
Package: knopt
knoptBuild command line applications by inspecting function signatures. Inspired byoptfunc.Quick startTo create a simple command line application, annotate a function withknopt.main:fromknoptimportmain@maindefcli(input,output,verbose=False):ifverbose:print('input:',input)print('output:',output)Command line arguments are automatically parsed usingargparseand passed to theclifunction.To use other features of theargparselibrary, you can use annotations on arguments. Pure type annotations work as expected:fromknoptimportmain@maindefdouble(number:int):print(number*2)but for more control, you can useknopt.argas an annotation and include options forargparse:fromknoptimportmain,arg@maindefsearch(directory:arg(help="directory to search"),invert:arg('v',help="invert match")=False,pattern:arg('e',help="search pattern")=''):"""search files in a directory"""print("searching",("~"ifinvertelse"")+repr(pattern),"in",directory)Note that the docstring ofsearchis used as a description for the program.CommandsYou can also create a program accepting multiple commands by using theknopt.mainannotation on a class. Every method of the class is converted to a command. For example:fromknoptimportmain@mainclassProgram:"""An example program with commands"""def__init__(self,verbose:arg('v')=False):self.verbose=verboseself.files=[]defadd(self,files:arg(nargs='*')):"""add files to a repository"""ifself.verbose:print('adding',files)defcommit(self,all:arg('a')=False):"""commit files"""ifself.verbose:print('committing')This creates a program with two commandsaddandcommit, and a global option-v/--verbose.
Package: knosk-core
knosk-coreknosk-core 1.0.0a1knosk-coreknosk-core is a dialog management framework.TBDInstallationInstall via pip:$ pip install knosk-coreInstall from source:$ git clone git@github.com:knosk/knosk-core.git
$ cd knosk-core
$ python setup.py installUsageTBDRunning tests$ python ./tests/test.py
Package: knossos
A modern mod manager and launcher for FreeSpace Open.After installing this package, you can launch Knossos withknossosorpython-mknossos.Website:https://dev.tproxy.de/knossos/Source Code:https://github.com/ngld/knossosDiscussion on HLP:http://www.hard-light.net/forums/index.php?topic=86364.new#lastPost