Saravanan666/pypi-org huggingface dataset:
Package: infinite-image-downloader
=====_Developer: Zhiao Zhou ([@zhiaozhou](https://github.com/zhiaozhou) | <zz1749@nyu.edu> | [Linkedin](https://www.linkedin.com/in/zhiaozhou/))_Infinite_image-downloader is a python library for automatically downloading requested number of images from Google image search by one keyword.The principle is that since Google has limited the number of outputs per keyword(1000 maximum), after getting numbers of pictures and their urls of the first search, the downloader is going to search more images using the urls that were just achieved and then recursively go on.Dependencies=============pgmpy has following non optional dependencies:- Python 2.7 or Python 3- request- selenium- socket- Geckodriver for Firefox usersInstallation=============Using pip:```$ pip install infinite_image_downloader```Or for installing the latest codebase:```$ git clone https://github.com/zhiaozhou/infinite_image_downloader.git$ cd infinite_image_downloader/$ python setup.py install```Documentation and usage=======================Now this package can be only used with a Firefox browser installed.Then you will have to install Geckodriver from [Geckodriver/releases](https://github.com/mozilla/geckodriver/releases) and then put the executable file inside the root folder of your Firefox directory (Ex. C:\Program Files\Mozilla Firefox\)For Mac users, put it under usr/local/binThen after installing the package using pipyou can start to use it by typing infinite_image_downloader [keyword] [numbers of images you want] [download path](optional)An example is shown below![example](example.gif)License=======infinite_image_downloader is released under MIT License.Keywords: google image crawl downloadPlatform: UNKNOWNClassifier: Development Status :: 3 - AlphaClassifier: Intended Audience :: EducationClassifier: Topic :: UtilitiesClassifier: License :: OSI Approved :: MIT LicenseClassifier: Programming Language :: Python :: 2Classifier: Programming Language :: Python :: 2.7Classifier: Programming Language :: Python :: 3Classifier: Programming Language :: Python :: 3.4Classifier: Programming Language :: Python :: 3.5Classifier: Programming Language :: Python :: 3.6Classifier: Operating System :: UnixClassifier: Operating System :: POSIXClassifier: Operating System :: Microsoft :: WindowsClassifier: Operating System :: MacOS
Package: infinite-note
infinite_note문제와 답을 입력하여 정답/오답을 관리하는 콘솔 프로그램설치https://pypi.org/project/infinite_note사용법inf-note-input = 문제 입력inf-note-count = 입력된 문제 갯수 확인inf-note-answer = 입력된 문제 출력 및 답안 저장개발자 가이드https://offtheface.github.io/infinite-note
Package: infinitesimals
A capable HyperReal class and related methods for work with infinitesimals and infinities in nonstandard analysis.Change Log0.1.0 (12/04/2023)Out of Pre-AlphaCode rewritten from ground-upRemoved reliance on matrix functions for calculationsAdded support for infinities (i.e; negative infinitesimal powers)Added limited support for fractional infinite / infinitesimal powersNew natural logarithm algorithm for higher precision calculations (used in a majority of functions)
Package: infinite-state-machine
python_state_machineAn Infinite State Machine implemented in pythonWhy "Infinite"?Finite state machines are a great method for implementing event driven applications and operating systems.
However, when implemented, they become fixed implementations of an application that do not easily lend themselves
to refactoring into another application.The Infinite State Machine implemented here uses "Action Packs" that can be easily shared with other
State Machines. By abstracting the actions required to implement specific functions, such as a message queue or
an automation framework, developing a state machine becomes as simple and familiar as writing a Python application that
imports its functionality in packages.Unit TestsRun the unit tests from the package root using this syntax:python3 -m unittest -v ism.tests.test_ism.TestISMTo clean down the unit test mysql databases created  log into mysql using the -s option to give clean result sets, then:mysql> SELECT CONCAT('DROP DATABASE ', SCHEMA_NAME, ';') FROM 
information_schema.schemata WHERE SCHEMA_NAME LIKE '%ism_default_%'; 

CONCAT('DROP DATABASE `', SCHEMA_NAME, '`;')
DROP DATABASE `ism_default_1611855489819`;
DROP DATABASE `ism_default_1611855533770`;
DROP DATABASE `ism_default_1611856233213`;
DROP DATABASE `ism_default_1611856253150`;
DROP DATABASE `ism_default_1611856264937`;Then copy and paste the drop statements into the command line.
Package: infinite-training
Infinite TrainingThe python package for training ML models base on loss, metrics and timeout. We can create target for training or using Ctrl + C to interrupt the training session. This package can store the last weight and optimize weight for us to continue training after interrupting.Using as example:"""Apply in example from https://www.tensorflow.org/datasets/keras_example"""importtensorflow_datasetsastfdsimporttensorflowastffrominfinite_trainingimportInfinityTraining,Target(ds_train,ds_test),ds_info=tfds.load('mnist',split=['train','test'],shuffle_files=True,as_supervised=True,with_info=True,)defnormalize_img(image,label):"""Normalizes images: `uint8` -> `float32`."""returntf.cast(image,tf.float32)/255.,labelds_train=ds_train.map(normalize_img,num_parallel_calls=tf.data.AUTOTUNE)ds_train=ds_train.cache()ds_train=ds_train.shuffle(ds_info.splits['train'].num_examples)ds_train=ds_train.batch(128)ds_train=ds_train.prefetch(tf.data.AUTOTUNE)ds_test=ds_test.map(normalize_img,num_parallel_calls=tf.data.AUTOTUNE)ds_test=ds_test.batch(128)ds_test=ds_test.cache()ds_test=ds_test.prefetch(tf.data.AUTOTUNE)model=tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape=(28,28)),tf.keras.layers.Dense(128,activation='relu'),tf.keras.layers.Dense(10)])it=InfinityTraining(model=model,target=Target(name="val_sparse_categorical_accuracy",smaller_is_better=False,target_value=0.98),timeout=100)it.compile(optimizer=tf.keras.optimizers.Adam(0.001),loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],)it.train(ds_train,validation_data=ds_test)it.show_result(ds_train)
Package: infinitum-sentry-auth-oidc
An SSO provider for Sentry which enablesOpenID ConnectApps authentication.This is a fork ofsentry-auth-google.Why fork, instead of adapting sentry-auth-google to work with every OpenID Connect provider?The maintainer has different ideas with sentry-auth-google. See:https://github.com/getsentry/sentry-auth-google/pull/29https://github.com/getsentry/sentry/issues/5650Install$ pip install sentry-auth-oidcExample Setup for GoogleStart bycreating a project in the Google Developers Console.In theAuthorized redirect URIsadd the SSO endpoint for your installation:https://sentry.example.com/auth/sso/Naturally other providers, that are supporting OpenID-Connect can also be used (like GitLab).Finally, obtain the API keys and the well-known account URL and plug them into yoursentry.conf.py:OIDC_CLIENT_ID=""OIDC_CLIENT_SECRET=""OIDC_SCOPE="openid email"OIDC_DOMAIN="https://accounts.google.com"# e.g. for GoogleTheOIDC_DOMAINdefines where the OIDC configuration is going to be pulled from.
Basically it specifies the OIDC server and adds the path.well-known/openid-configurationto it.
That’s where different endpoint paths can be found.Detailed information can be found in theProviderConfigspecification.You can also defineOIDC_ISSUERto change the default provider name in the UI, even when theOIDC_DOMAINis set.If your provider doesn’t support theOIDC_DOMAIN, then you have to set these
required endpoints by yourself (autorization_endpoint, token_endpoint, userinfo_endpoint, issuer).OIDC_AUTHORIZATION_ENDPOINT="https://accounts.google.com/o/oauth2/v2/auth"# e.g. for GoogleOIDC_TOKEN_ENDPOINT="https://www.googleapis.com/oauth2/v4/token"# e.g. for GoogleOIDC_USERINFO_ENDPOINT="https://www.googleapis.com/oauth2/v3/userinfo"# e.g. for GoogleOIDC_ISSUER="Google"
Package: infinity-coin
infinity_coininfinity_coin- это python модуль для работы с монетой Infinity Сoin (Infinity Сoin API wrapper)Примеры использования(python3)Документация по методам APIИспользование:Для начала нужно получить api ключ, для этого отправьтеботусообщений с текстом «!api»frominfinity_coinimport*InfinityCoinObj=InfinityCoin('API_KEY')Данная функция возвращает значение равное балансу вашего аккаунта:score=InfinityCoinObj.score()Данная функция возвращает информацию о 100 последних платежных операциях, совершенных с участием вашего аккаунта:forpaymentinInfinityCoinObj.history():print(payment)Данная функция используется для перевода Infinity Coin с вашего баланса на баланс пользователя, id которого указан в параметре 'toid', а сумма в параметре 'amount':InfinityCoinObj.transfer(toid=139858119,amount=1000)Данная функция генерирует быструю ссылку для перевода пользователю:fast_link=InfinityCoinObj.get_fast_link(toid=139858119)Данная функция генерирует быструю ссылку для перевода пользователю фиксированной суммы:fast_link=InfinityCoinObj.get_fast_link(toid=139858119,amount=1000)Из коробки Infinity Coin не предоставляет longpoll/callback, но infinity_coin предоставляет небольшой костыль, аля longpoll:longpoll=InfinityCoinPoll(InfinityCoinObj)foreventinlongpoll.listen():print(event)InfinityCoinPoll будет обновлять историю и возвращать новые транзакции каждые 3 секунды, но это можно изменить передав в него параметр timeout:longpoll=InfinityCoinPoll(InfinityCoinObj)foreventinlongpoll.listen(timeout=10):# обновление будет происходить каждые 10 секундprint(event)Установка:$ pip install infinity_coinВажно!Я не являюсь разработчиком Infinity Coin и не знаком с ними. Модуль infinity_coin не гарантирует постоянную и стабильную работу.Связь:ВКонтакте:vk.com/mrsmitix
Package: infinitydb
InfinityDB NoSQL DBMS REST AccessThe infinitydb.access module provides a REST interface to the APIs
defined in the server by means of PatternQueries.
Each access point looks like:https://myserver.com/infinitydb/data/my/db/"my.interface"/"myquery"?action=execute-queryWhere my/db is the name of a database in the server, "my.interface"
is the name of an interface in the server, and "myquery" is
appended to the interface to uniquely identify the query.
There can be JSON request content and response content as well.
Sometimes port 37411 is used.
The user name and password are provided in the authentication
header:Authorization: Basic <base64 credentials>The actual access is done through the module's convenience
functions. Also, the data representation of 'Items' is
more general than JSON, so there are functions to
convert from JSON to Python dicts and deal with
tuples.Seeboilerbay.comfor more.
Package: infinity-sdk
python-infinitybuildrm-fdist/*&&pythonsetup.pysdistbdist_wheelinstallpipuninstall-yinfinity-sdk&&pipinstalldist/*.whlThis will install infinity-sdk and its dependencies.upload to pypi.orgtwineuploaddist/*.whlEnter your pypi API token according to the prompt.Note that pypi allow a version of a packagebe uploaded only once. You need to change theversioninside thepyproject.tomlbefore build and upload.usingimportinfinityfrominfinity.commonimportREMOTE_HOSTinfinity_obj=infinity.connect(REMOTE_HOST)db=infinity_obj.get_database("default")db.drop_table("my_table",if_exists=True)table=db.create_table("my_table",{"num":"integer","body":"varchar","vec":"vector,5,float"},None)table.insert([{"num":1,"body":"undesirable, unnecessary, and harmful","vec":[1.0]*5}])table.insert([{"num":2,"body":"publisher=US National Office for Harmful Algal Blooms","vec":[4.0]*5}])table.insert([{"num":3,"body":"in the case of plants, growth and chemical","vec":[7.0]*5}])res=table.output(["*"]).knn("vec",[3.0]*5,"float","ip",2).to_pl()print(res)For developerpipinstall-e.
Package: infinopy
InfinoPyPython Client for Infino, a scalable telemetry store.Quick Startinfinopyis a python client for Infino. Infino is a scalable telemetry store to reduce the cost and complexity of observability. If you haven't explored Infino yet, please refer to theInfino git repo.Install InfinoPy ClientpipinstallinfinopyNotes for testingYou should ensure your environment is up-to-date if you want to edit/test the client:OpenSSL3,Python3, Virtualenv, Setuptools. If you've changed any of the core Infino code you'll also need to runmake docker-buildto build the docker image that the infino server needs to run in for unit tests.ExampleThe documentation is still in progress. In the meantime,this testillustrates how to use InfinoPy - the Python client for Infino.
Package: infiray-irg
Infiray IRG file format parserThis python module contains a simple parser for the "IRG" file format that the Infiray C200 series thermal cameras use
to dump their raw data. The files contain three things: A thermal image with 8 bit resolution with contrast scaled to
fit the 0...255 range, a thermal image with 16 bit resolution containing absolute temperature values with 1/16 K
resolution, and a JPEG with the image from the low-res visual camera.Requirementspillow and numpyAPICallinfiray_irg.load(data)with abytesobject containing the IRG file's contents. It will return a tuple(coarse, fine, vis)of the coarse and fine images as numpy arrays, followed by the visual image as a pillow image. The coarse
image has dtype uint8 and contains values from 0 to 255, where 0 is the coldest pixel in the image, and 255 is the
hottest. The fine image has dtype float and contains absolute degree Celsius values.ExamplefrommatplotlibimportpyplotaspltfrompathlibimportPathimportinfiray_irgcoarse,fine,vis=infiray_irg.load(Path('example.irg').read_bytes())fig,((ax1,ax2),(ax3,ax4))=plt.subplots(2,2,figsize=(15,18))ax1.imshow(coarse)ax1.set_title('Coarse contrast-maximized')fine_plt=ax2.imshow(fine)ax2.set_title('Fine absolute temperatures')fig.colorbar(fine_plt,ax=ax2,location='right',label='degrees Celsius')ax3.imshow(vis)ax3.set_title('Visual')ax4.hist(fine.flatten(),bins=100)ax4.set_title('Temperature histogram')ax4.set_xlabel('degrees Celsius')fig.tight_layout()fig.savefig('plot.png')print('Coldest pixel:',fine.min(),'C','Hottest pixel:',fine.max(),'C')BugsIf you find a bug, or find a file that this library can't load, please send me an email atcode@jaseg.de.LicenseThis module is licensed under the MIT license.
Package: infiray-lrf
InfiRay-LRFCommand sender for the InfiRay LR2000 / LR3000 Long Range FinderInstalationpipinstallinfiray-lrfUsageRun console# enter infiray-inf UART consolepython-minfiray-lrfCommands list# commands list:q-quit
h-helpi-selfinspection

s-singleranging
cf<int:frequency><int:timeout>-continiousranging(frequency1~10Hz,timout-seconds)C-nonstopcontiniousranging
b-stopranging
freq<int:frequency>-setcontiniousrangingfrequency(1~10Hz)glg-queryminimumgatingdistance
ghg-querymaximumgatingdistance
slg<int:distance>-setminimumgatingdistance(10-20000m)shg<int:distance>-setmaximumgatingdistance(10-20000m)fpga-queryFPGAsoftwareversionnumber
mcu-queryMCUsoftwareversionnumber
hw-queryhardwareversionnumber
sn-querySNnumber

total-countoftimesoflightresput
count-countoftimesoflightresputsinceLRFpowerup

setbaudrate<int:baudrate>-setbaudratevalue(115200/57600/9600)
Package: infisical-api
infisical_apiinfisical_api is a python client for the infisical REST API. I found some of the dependencies int he infisical_python client incompatible with some of my applications so I build this very simple version that utilizes the REST API.Get Secretfrominfisical_apiimportinfisical_apisecrets=infisical_api(service_token=token,infisical_url=url)# infisical_url is only needed if using self-hostedusername=secrets.get_secret(secret_name="USERNAME",path="/MYSQL").secretValue# path defaults to "/" when not specified
Package: infi.traceback
Tracebacks in Python are missing some useful debugging information, such is locals() in each stack frame
This module provides several mechanisms for better tracebacks:
* traceback_context. A context that in patches standard’s library traceback module to print better tracebacks
* traceback_decorator. A decorator that calls the decorated method inside the traceback_context
* Nose Plugin. The plugin, enabled with ‘–with-infi-traceback’, prints a better traceback for errors and failures
* pretty_traceback_and_exit_decorator. A decorator for console script entry points that prints exceptions and raises SystemExit
Package: infix-postfix
An application to convert infix to postfix and vice versa...Key FeaturesRuns standaloneConverts Infix expression to postfix expression.Converts postfix expression to infix expression.Basic Requirements (Pre-installed)Python 3.xWindows OS or MacOS or Other Linux Distribution.InstallationOpen terminal/cmd in windows/macos respectively.Run the commandpip install infix-postfix.UsageTo convert infix to postfix, runinfix-postfix --infix "${infix_expression}"To convert postfix to infix, runinfix-postfix --postfix "${postfix_expression}"NOTE : Double quotes ("") is must for writing the expression to get correct output..How to write expressions? 
- INFIX expression
    - Correct Methods
        - "( A+B)* C/D"
        - "((A+B)*C/D+E^F)/G"
        - "20.1 + (5 ^ 2 / 2)"
        - "20.1 + ( 5 ^ 2 / 2)"
        - etc...

- POSTFIX ecpression
    - Correct Methods
        - "A B + C * D /"
        - "A B C D + E F + * G / + * H *"
        - "20.1 5 +"
    - Wrong Methods
        - "AB+C*D/"
        - "ABCD+EF+*G/+*H*"
        - "20.1 5+"
        - "20 5+"
    - In One line, You have to give spaces after every valueTo run this application in your own application or python script, runfrominfix_postfix.infix_postfiximportinfix,postfix$(variable)=$(infix/postfix){expression}print($(variable))Example :-ContributionAnyone is free to contribute to this open source project. 🎉🎉
Package: infixpy
infixpySee blog post,Introducing infixpy: Scala-inspired data structures for Pythonto learn about using this library.A functional, object-oriented approach for working with sequences and collections. Also similar to Spark RDDs and Java Streams. Hope you find they simplify your code by providing a plethora of common algorithms for working with sequences and collections.Stephen's Direct from Scala exampleScala versionvala=((1to50).map(_*4).filter(_<=170).filter(_.toString.length==2).filter(_%20==0).zipWithIndex.map{case(x,n)=>s"Result[$n]=$x"}.mkString("  .. "))a:String=Result[0]=20..Result[1]=40..Result[2]=60..Result[3]=80Version using the infixpy library with pythonfrominfixpyimport*a=(Seq(range(1,51)).map(lambdax:x*4).filter(lambdax:x<=170).filter(lambdax:len(str(x))==2).filter(lambdax:x%20==0).enumerate().map(lambdax:'Result[%d]=%s'%(x[0],x[1])).mkstring(' .. '))print(a)# Result[0]=20  .. Result[1]=40  .. Result[2]=60  .. Result[3]=80Original ExamplefrominfixpyimportSeq(Seq(range(10)).map(lambdax:x+3).filter(lambdax:x%2==0).groupby(lambdax:x%3).keys().foreach(print))Output1
0
2ExamplesSee examples/ directory for additional examples of using infixpy.Also see example usages incareer_village_entities.
Package: inflame
1. The problem this project is trying to solveCan you easily repurpose the deep learning model you just built to new problems?Is the process of converting a deep learning model from development to production a pain point for you?How maintainable is your deep learning model code base?2. The designThe idea of the inflame project (built on top of PyTorch) is to extend PyTorch by allowing one to easily create templates of the deep learning models. Most deep learning model codes are basically prototypes. Having evolved from multiple iterations of trial and error, deep learning model codebases tend to have a lot of hardcoding of values, and they tend to be large single-file beasts. The result is that they are hard to understand, debug, and reuse on new problems. Inflame helps solve this problem by organizing the deep learning model code around concerns. The data, model architecture, hyperparameter, backpropagation, and accuracy evaluation -- each of these domains of functionality is its own module. By organizing and API-ing the code via design patterns, the same deep learning model can be reused from project to project with the smallest number of obvious customizations.3. Installpipinstallinflame4. Quick startTo run the demo project that classifies news articles using convolutional net:inflame_run--corpusbbcnewsTo run the demo project that classifies newsgroup posts using convolutional net:inflame_run--corpusnewsgrp
Package: inflate
:money_with_wings: inflateinflate is a Python built command line interface that lets you quickly compute how inflation affects a sum of money in an intuitive and visually pleasing package.InstallationRunpip install inflate. Verify that the latest version is installed by runninginflate --version.Usage>>>inflate[OPTIONS]STARTEND[AMOUNT][AMOUNT]in[START]isworth[INFLATEDAMOUNT]in[END]([INCLUSIVE/EXCLUSIVE])The options are-c, --country TEXT  Country [either US/UK], default is UK
  -i, --inclusive     Include ending year, default no
  -a, --average       Show average annualised inflation, default no
  --version           Show the version and exit.
  --help              Show this message and exit.Example:>>>inflate-cUS-i1998199950$50.0in1998isworth$51.51in1999(inclusive).
>>>inflate-a1998200050£50.0in1998isworth£51.46in2000(exclusive).Theaverageinflationacrossthisperiodwas1.46%.Current featuresAble to work "backwards", that is, if you want to know how much $10 in 2018 was worth in 2000, you can do so viainflate -c US 2018 2000 10Package and publish on PyPI.Future features/workAdd in a more detailed analytics view, showing average inflation across time period.Flesh out the testing modules
Package: inflate64
Theinflate64is a python package to provideDeflaterandInflaterclass to compress and
decompress with Enhanced Deflate compression algorithm.The project is inProduction/Stablestatus.How to useYou can install it withpipcommand as usual.pip install inflate64You can extract compressed data by instantiatingInflaterclass and callinflatemethod.importinflate64decompressor=inflate64.Inflater()extracted=decompressor.inflate(data)You can also compress data by instantiatingDeflaterclass and calldeflatemethod.importinflate64compressor=inflate64.Deflater()compressed=compressor.deflate(data)compressed+=compressor.flush()LicenseThis library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
Lesser General Public License for more details.You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USANotePlease note that Enhanced Deflate algorithm is also known asDEFLATE64TMthat is a registered trademark ofPKWARE, Inc.