Saravanan666/pypi-org huggingface dataset:
Package: ihttpy
HTTP ServerServer which runs Hyper Text Transport ProtocolCheck it out atPYPIServer parametersconfig=ConfiguratorSpecify a configurator. UseFluentConfiguratorfor
type hints orConfiguratorfor plain textloglevel=LogLevel.loggingChoose a logging or console form reportingrefresh_rate=0.1Socket connection refresh ratecache_max_size=4e9Max cache sizeDownloadpipinstallihttpyFluent Exampleimport...config=FluentConfigurator()@config.on(Method.GET).at('/')defindex(req:Request,srv:Server)returnResponse(200,'OK')config._host='0.0.0.0'config._port=8000server=Server(config,loglevel=LogLevel.CONSOLE)withserverass:s.run()Running the testspython3-munittestsdiscovertests/Plaintext Config settingsrulesIs a map with regular languageuse square braces for any match (same as .*? in re)type [name] inside braces to set named groupuse match names in found path
example:"/[day]-[n]/[month]/[year]" : "/pictures/[year]/[month]/[day]/[n].pngquerylocalhost:8000/27-me/09/2000will display
a photo/pictures/2000/09/27/me.pngif existshostChose server hostuselocalhostto run locallyportSpecify port for server to listendefault is8000error-pagesused by Errors classDynamic handlers configurationif you want more handling control:specifypathfield for static file responseusehandlerobject description assourceto choose handler pathpostname of function to handle POST requestgetname of function to handle GET requestspecifyheadersfor additional headers to be addedstatic file responselocalhost:8000/my_guest_bookurl"/my_guest_book": "tmp/my_guest_book.html",router will match this url and send filetmp/my_guest_book.htmlcustom GET handler forlocalhost:8000/postsurl"/posts": {
  "handler": {
    "source": "handlers/my_guest_book.py",
    "get": "get_posts"
  }
}this will start loadedsourcehandler search for url and callget_postsfunctioncustom POST handler forlocalhost:8000/posturl"/post": {
   "handler": {
     "source": "handlers/my_guest_book.py",
     "post": "handle_post"
   }
 }it will callhandle_postfunction fromsourcefound handler and process
request in that moduleDynamic handlers usageHandlers should be added as modules with 1 or 2 functions (which are
configured ifconfig.jsonas it was described inhere)Its signature needs to bedef function(request: Request, server: Server) -> Response:Here you can use Server and Request that is being processedRequeststructure:self.method- request method (GET/POST)self.target- urlself.version- HTTP versionself.headers- headers dictionaryself.body- request bodyUse this properties as you need to process the requestLogging and debugIf you want std.out as primary output use-l consoleIf you want file as output use-l loggingThere is alogger.pyfor server info logging and debug, configure
LOGGER_PATH and LOG_DEBUG_PATH indefenitions.pyAuthorRuslan Sirazhetdinov-Project creator, UrFU StudentSupervisorViktor Samun-UrFU Python headquaters
Package: ihu
ihuEvolve any unitary using solve_ivpThe package will allow you to evolve a unitary, give the
Hamiltonian. We assume closed system dynamics.
So, no lindbladians allowed !!!
One needs to giveunitary at time zero,Hamiltonian as a function which depends on time.evolution timeFrom the unitary at time zero we will determine the
shape of the Unitary.
We hope that the hamiltonian that you provide will
be of same shape as that of the unitaryInstallpip install ihuHow to useFill me in please! Don't forget code examples:1+12
Package: ihypy
# ihypy
WIP Python musical analysis module# Installpy -m pip install ihypyfor Windowspython3 -m pip install ihypyotherwise# Description
Where does the name come from? I wanted something musical that rhymed with py, and Ihy apparently is an Egyptian god of music—more specifically, the ectasy of playing the sistrum. Whether or not that actually rhymes, I am pronouncing the name of this repository as “I Hi Py” as it looks like it should rhyme, and the sistrum’s heiroglyph looks cool. 𓏣# Purpose
More than anything else, I want to familiarize myself with how to do stuff on Github but keeping it interesting by doing musicology stuff. I want to create a framework that allows for the creation of many different ethnic music background to be explored, while providing just some basic Western classical instances.# Framework
I will be trying to use abc.ABC for abstraction and docstring/type annotations. I am honestly still not really used to doing this in Python, but I want to try at least, even if the docstrings are poorly written for now.There will only be a few base/abstract classes that really lay out the groundwork as I get started: Note, NotationError, TuningSystem, NotationSystem, and MusicalSystem. MusicalSystem itself is really built out of the combination of a TuningSystem and NotationSystem, and everything else will be derivative from that.I have not come up with any functional interfaces yet, though they might come if necessary. At the moment, I have not thought of such an example unless I introduce different units of measure in music (e.g. Hz vs. cent or comparing enharmonic pitches), but at the moment, I do not want to tie my hands up in that mess.# Systems
My goal is to first create a couple of simple types of systems.Tuning
- 12 TET
- Just intonation
- Pythagorean intonationNotation
- IPN
- Helmholtz pitch notationMusical
- Western classical# Audio and Transcription
There will likely be some audio based stuff too. If I can create a note, it would be nice to hear it. I could turn this into pseudo musical notation if I can define something like a Piece class that describes when and what notes should be played. If nothing else, it would be nice to be able to break down chords.If possible, I would also like to be able to transcribe from any of the following sources for analysis:
- Sheet music
- MIDI
- Audio (i.e. actual recordings)I do not ever plan to create something that write sheet music.# Testing
I will also attempt to run tests. I will undoubtedly miss many things, but that is ok. I just want to learn how to write tests and improve at it. I will test stuff along the way, but I will run larger scale tests as well.# Time Frame
In terms of scheduling and timing, I am in no rush. This is a project I am doing for fun, so updates will likely not be regular. However, I hope that I will not stall the project for a long time.It would be nice to have at least some of audio/transcription stuff along with tuning, notation, and musical systems done in a month-ish, but who knows? If I get that, I might be able to make a first release of a module, but we shall see.
Package: iias-project-cli
project_cliA commandline interface for creating structured Python projects.Installationpip install project_cliUsagepython -m project_cliIf you trustproject_clito make the right decisions, you can say ‘yes’ to all prompts:python -m project_cli -yDetailed informationHere is an example project structure created by this tool for a runnable project:> proj_name
    __init__.py
    __main__.py
> tests
    __init__.py
    context.py
    test_main.py
README.rst
setup.pyYou can run your app as follows:python -m proj_nameYou can run tests as follows:python -m unittest discover testsYou can also install your project in your own pip repository (or in a virtual environment) to make it runnable anywhere on your system:pip install -e .Note: the-eflag is optional. It will keep your pip repository synchronized with your source code.MetaRamon Hagenaars -ramon.hagenaars@gmail.comThis structure was inspired byKenneth Reitz.
Package: iic4sem
#### IIC4SemThis project is for the academic requirement of semester 4 exams of IIC, UDSC.#### InstallationCreate a virtualenv to install on a clean environment.```$ virtualenv -p python3 venv$ source venv/bin/activate```To install this package, use pip.```pip install iic4sem```Then simply run `iic4sem` from the command line.#### What is missing?* Unit Tests* Project Work* Question 5 - That has to be done in C#### Sample Output```$ iic4sem_____ _____ _____ _  _   _____|_   _|_   _/ ____| || | / ____|| |   | || |    | || || (___   ___ _ __ ___| |   | || |    |__   _\___ \ / _ \ '_ ` _ \_| |_ _| || |____   | | ____) |  __/ | | | | ||_____|_____\_____|  |_||_____/ \___|_| |_| |_|Version: 0.1.0Made by Abhinav SaxenaFor Mr. Anil Singh Bafila (c/o Dr. Sanjeev Singh)Institute of Informatics and Communication,University of Delhi, South Campus[INFO:2018-03-21 01:51:29,353]:root: Starting IIC4Sem using configuration found in: iic4sem.cfg[INFO:2018-03-21 01:51:29,355]:iic4sem.echo_server: Initializing Simple Echo Server![INFO:2018-03-21 01:51:29,356]:root: Found and enabled ('echo', <class 'iic4sem.echo_server.EchoServer'>) protocol.[INFO:2018-03-21 01:51:29,356]:root: Found and enabled ('libevent', <class 'iic4sem.libevent_server.LibeventServer'>) protocol.[INFO:2018-03-21 01:51:29,357]:root: Found and enabled ('http', <class 'iic4sem.http_server.HTTPServer'>) protocol.[INFO:2018-03-21 01:51:29,357]:root: Found and enabled ('broadcast', <class 'iic4sem.broadcast_server.BroadcastServer'>) protocol.[INFO:2018-03-21 01:51:29,358]:iic4sem.echo_server: EchoServer waiting for a connection on ('127.0.0.1', 16000)[INFO:2018-03-21 01:51:29,358]:iic4sem.libevent_server: Starting Libevent Server on port 16000[INFO:2018-03-21 01:51:29,358]:iic4sem.http_server: Starting HTTPServer on ('127.0.0.1', 16003)[INFO:2018-03-21 01:51:29,375]:iic4sem.broadcast_server: Starting BroadcastServer on ('127.0.0.1', 16002)```
Package: iidxbak
本项目用于在centos上备份信息使用步骤mkdir -p ${HOME}/.iidxbak/workpushd ${HOME}/.iidxbak/workpython3 -m pip install pipenv==2020.11.15pipenv install --indexhttps://pypi.org/simpleiidxbak==0.9.5 --skip-lockpipenv run iidxbak --dir_to_bak some_path_to_bakconfig_demo默认软件家目录：${HOME}/.iidxbak/默认配置文件位置：${HOME}/.iidxbak/conf/iidxbak.json默认工作目录：${HOME}/.iidxbak/work/{20231111-1010}/{"public_key":"conf/keys.public.pem","work_dir":"work/","oss_ak_id":"XXXX","oss_ak_secret":"XXXX","oss_ep":"https://oss-cn-hangzhou.aliyuncs.com","oss_bucket_name":"XXXX","i_node_info":true}项目依赖click==8.1.7sh==2.0.6oss2==2.18.3环境安装# 0. uname -auname-a# Linux app-001-156 3.10.0-1160.76.1.el7.x86_64 #1 SMP Wed Aug 10 16:21:17 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux# 1. sudo yum install -y python3-devel# 2. ssh -Vssh-V# OpenSSH_7.4p1, OpenSSL 1.0.2k-fips  26 Jan 2017# 3. openssl versionopensslversion# OpenSSL 1.0.2k-fips  26 Jan 2017# 4. python3 --versionpython3--version# Python 3.6.8
Package: iiif
iiif- IIIF Image API reference implementation and Python library.
Supports theInternational Image Interoperability Framework: Image API2.1and3.0(as well as legacy versions2.0,1.1,1.0).
Also includes a test server using the library to implement the Image
API transformations dynamically, and a static file generator to
pre-compute tiles necessary for OpenSeadragon which may then be
stored a static files.InstallationThe library, test server, static file generator are all designed to
work with recent versions of Python 3 and Python 2.7. Manual installation
is necessary to get the demonstration documentation and examples.Automatic installation from PyPITheiiiflibrary code and scripts are available fromPyPIand can be installed withpip, e.g.:pip install iiifThe PyPI version does not include all demonstration documentation and
examples.Manual installation from githubFirst, clone the code and examples from theGithub repository:cd /tmp
git clone git://github.com/zimeon/iiif.git
cd iiif/Second, installPillow(a fork ofPIL):pip install PillowYou may needsudoif installing system-wide as opposed to in a user-space
environment.After installation ofPillowthere will be be a summary of image formats
supported. Addition libraries may be require to get all formats depending
on your platform.The scripts and demonstrations can then be run from theiiifdirectory.Finally, you may install the library code and scripts locally in the
appropriate places within your python setup using:python setup.py build
python setup.py installYou may needsudoto if installing system-wide as opposed to in a user-space environmentThe source code is maintained onGithubContributingBug reports welcome asgithub issues.SeeCONTRIBUTING.mdfor guidelines for contributing, and thelist of contributorsSee alsohttps://github.com/zimeon/iiif– git repositoryhttps://github.com/zimeon/iiif/tree/main/demo-static– Static File Demo with OpenSeadragonhttps://github.com/zimeon/iiif/tree/main/demo-auth– IIIF Authentication API DemoCopyright and Licenseiiif library and programs implementing the IIIF API
Copyright (C) 2012–2021 Simeon WarnerThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program 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 General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.SeeLICENSE.txt
Package: iiif2
An implementation of the IIIF Image API 2.0 SpecificationInstallation$pip3installiiif2UsageThe iiif2 library includes an image processing component called IIIF,
responsible for implementing the iiif image processing pipeline, and a
Parse utility capable of extracting iiif parameters and their data
from uris.fromiiif2importIIIFfromiiif2.webimportParseYou can combine the IIIF and Parse objects to create iiif 2.0 image
tiles using only:a iiif image 2.0 uri anda resolved image filepathAn image path may be provided in one of two ways. First, it can be
manually specified as a string:fromiiif2importIIIF,weburl='https://stacks.stanford.edu/image/iiif/''ff139pd0160%252FK90113-43/full/full/0/default.jpg'# a web server can return a rendered tile directly# without ever saving tile to disk. Works on read-only fs:tile=IIIF.render('images/file.jpg',*web.Parse(url))# if we want, we can save tile (e.g. for caching)tile.save('cache/%s'%web.urihash(url))Example Web ServiceAn entire IIIF Web Service written in Flask in ~30 lines of code is
provided in the examples/ folder.importos.pathfromflaskimportFlask,request,jsonify,send_filefromiiif2importIIIF,webPATH=os.path.dirname(os.path.realpath(__file__))app=Flask(__name__)@app.route('/<identifier>/info.json')definfo(identifier):returnjsonify(web.info(request.url_root,identifier))@app.route('/<identifier>/<region>/<size>/<rotation>/<quality>.<fmt>')defiiif(**kwargs):params=web.Parse.params(**kwargs)path=resolve(params.get('identifier'))withIIIF.render(path,**params)astile:returnsend_file(tile,mimetype=tile.mime)defresolve(identifier):"""Resolves a iiif identifier to the resource's path on disk.
    This method is specific to this server's architecture.
    """returnos.path.join(PATH,'images','%s.jpg'%identifier)if__name__=="__main__":app.run(debug=True)
Package: iiif-image-load
iiif_image_loadA python library for loading iiif images (IIIF Image Api Manifests).How to InstallTo install, just runpip install iiif_image_load.AboutThis library makes it possible to submit the URL to a IIIF Image API manifest (along with optional cropping parameters) and receive a standard OpenCV Numpy (3D BGR) array containing the image.The downloading process will access image tiles in accordance with the IIIF Image server's specifications. This makes download of image at full resolution possible on all IIIF compliant image servers and will potentially speed up the download process (and relieve stress on the IIIF image server).UsageTo acquire an image with this library, the following examples should suffice:fromiiif_image_loadimportiiif_image_from_urlimportcv2img=iiif_image_from_url('https://ids.lib.harvard.edu/ids/iiif/47174896')cv2.imshow('Demo Paul Gauguin',img)cv2.waitKey(0)cv2.destroyAllWindows()It is also possible to specify a crop box for the image:fromiiif_image_loadimportiiif_image_from_urlfrommatplotlib.pyplotimportimshowimg=iiif_image_from_url('https://ids.lib.harvard.edu/ids/iiif/47174896',10,20,500,500)imshow(img[...,::-1])
Package: iiif-prezi
iiif-prezi- A Python library providing a reference implementation
of theInternational Image Interoperability Framework:Presentation API 2.0(and earlier versions).InstallationThe library, tests and examples are designed to work with Python 2.7,
3.5, 3.6 and 3.7.Automatic installation from PyPITheiiif-prezilibrary code and scripts are listed inPyPIand can be installed withpiporeasy_install, e.g.:easy_install iiif-preziManual installation from githubFirst, clone the code and examples from theGithub repository:cd /tmp
git clone git://github.com/iiif-prezi/iiif-prezi.git
cd iiif/Second, installPillow(a fork ofPIL):sudo pip install PillowAfter installation ofPillowthere will be be a summary of image formats
supported. Addition libraries may be require to get all formats depending
on your platform.Finally, you may install the library code and scripts locally in the
appropriate places within your python setup using:python setup.py build
sudo python setup.py installThe source code is maintained onGithubUsageSee theCode walkthroughAuthors and ContributorsAuthors:Rob Sanderson,Simeon WarnerContributors:
seeGithub contributors graphContributingChange log
Package: iiii
iiii [Four Eyes] Search EngineFour Eyes is a search engine that helps you look wider and deeper.InstallationUse Python's virtual environment module to create your build environment, then withpipinstall the requirements file. Next, install Rust-nightly from the official website and ensure your$PATHis properly configured for your system. With the Rust package managercargo, build the binary folder targets.python3-mvenv.&&sourcebin/activate
python3-mpipinstall-rrequirements.txt
cargobuild--release
cargoinstall--path.Usageuser@computer$iiii[iiii]:<coolinternetthing>
...[RESULTS]...
user@computer$sudoiiii-f<randomthingonfilesystem>
...[FILEMATCHES]...
user@computer$iiii-dl<favoritememes>
...[RESULTS]...LicenseMIT
Package: i-impute
I-Impute: a self-consistent method to impute single cell RNA sequencing dataI-Impute is a “self-consistent” method method to impute scRNA-seq data. I-Impute leverages continuous similarities and dropout probabilities and refines the data iteratively to make the final output "self-consistent". I-Impute exhibits robust imputation ability and follows the “self-consistency” principle. It offers perspicacity to uncover the underlying cell subtypes in real scRNA-Seq data.Pre-requirementspython3numpy>=1.16.1pandas>=0.23.4,<0.24scipy>=1.3.0scikit-learn>=0.21.1tasklogger>=0.4.0install requirementspipinstall-rrequirements.txtInstallationInstallation with pipTo install with pip, run the following from a terminal:pipinstalli-imputeInstallation from GithubTo clone the repository and install manually, run the following from a terminal:gitclonehttps://github.com/xikanfeng2/I-Impute.gitcdI-Impute
pythonsetup.pyinstallUsageQuick startThe following code runs I-Impute on simulation data located in the I-Impute repository.importiimputeimportpandasaspd# read your reads count or RPKM or TPM datadata=pd.read_csv('simluation-data/sim-counts.csv',index_col=0)# create I-Impute objectiimpute_operator=iimpute.IImpute(normalize=False)# imputeimputed_data=iimpute_operator.impute(data)# store result to a fileimputed_data.to_csv('your file name')# iterative modeiimpute_operator=iimpute.IImpute(normalize=False,iteration=True)# imputeimputed_data=iimpute_operator.impute(data)# store result to a fileimputed_data.to_csv('your file name')ParametersIImpute(n=20,c_drop=0.5,p_pca=0.4,alpha=0.01,normalize=True,iteration=False,verbose=1)Parametersn : int, optional, default: 20The nth of nearest neighbors on which to build kernel when calculating affinity matrix.c_drop : float, optional, default: 0.5Dropout event cutoff. For entry whose dropout probability is less than c_drop, we consider it as a real observation, its original value will remain. Otherwise, we conduct the imputation with the aid of information from similar cells.p_pca : float, optional, default: 0.4Percentage of variance explained by the selected components of PCA. It determines the nmumber of PCs used to calculate the distance between cells.alpha : float, optional, default: 0.01L1 penalty for Lasso regression.normalize : boolean, optional, default: TrueBy default, I-Impute takes in an unnormalized matrix and performs library size normalization during the denoising step. However, if your data is already normalized or normalization is not desired, you can set normalize=False.iteration : boolean, optional, default: FalseThe imputation process only performs once when False (it is equivalent to C-Impute described in our paper). The imputation process will iterate n times to achieve self-constistent imputation matrix.verbose :intorboolean, optional, default: 1IfTrueor> 0, print status messagesCite usFeng, X., Chen, L., Wang, Z., & Li, S. C. (2019). I-Impute: a self-consistent method to impute single cell RNA sequencing data. bioRxiv, 772723. doi:https://doi.org/10.1101/772723.HelpIf you have any questions or require assistance using I-Impute, please contact us withxikanfeng2@gmail.com.
Package: iinfer
iinfer (Image Inference Application)onnx又はmmlabフォーマットのAIモデルファイルを実行するアプリケーションです。ドキュメントはこちら。iinferを使用することで、AIモデルを簡単に実行することが出来ます。動作確認したモデルは動作確認済みモデルに記載しています。主なAIタスクは、画像分類、物体検知、顔検知、顔認識です。複数のiinferコマンドの入出力をつなげる、パイプライン処理を行うことが出来ます。GUIモードを使用することで、iinferコマンド操作を簡単に行うことが出来ます。iinferの動作イメージiinfer clientはimageファイルやcameraから画像を取得し、推論結果 predict.jsonを出力します。iinfer serverは推論を行うサーバーです。iinfer clientからの要求に応えて、推論結果をiinfer clientに返します。iinfer serverは予めai modelをロードしておくことで、推論を高速化します。iinfer clientとiinfer serverはRedis経由で通信します。iinfer serverとRedisはdockerコンテナを使用して起動させることが出来ます。インストール方法インストール方法はこちらを参照してください。iinferの使用方法iinferを使用するには、次のコマンドを実行します:guiモードで利用する場合:iinfer-mgui-cstartコマンドモードで利用する場合AIモデルのデプロイ:# 画像AIモデルのデプロイ# 推論タイプはモデルのAIタスクやアルゴリズムに合わせて指定する。指定可能なキーワードは"iinfer -m client -c predict_type_list"コマンド参照。iinfer-mclient-cdeploy-n<任意のモデル名>-f\--model_file<モデルファイル>\--model_conf_file<モデル設定ファイル>\--predict_type<推論タイプ>\--label_file<ラベルファイル># デプロイされている画像AIモデルの一覧iinfer-mclient-cdeploy_list-fAIモデルのセッションを開始:# 画像AIモデルを起動させて推論可能な状態に(セッションを確保)する# use_trackを指定するとObjectDetectionタスクの結果に対して、MOT（Multi Object Tracking）を実行しトラッキングIDを出力する。iinfer-mclient-cstart-n<モデル名>-f\--use_track推論を実行:# 推論を実行する# output_previewを指定するとimshowで推論結果画像を表示する（GUI必要）iinfer-mclient-cpredict-n<モデル名>-f\-i<推論させる画像ファイル>\-o<推論結果の画像ファイル>\--output_preview# カメラキャプチャー画像を元に推論を実行し、クラススコアが0.8以上の物体のみを検出する# --stdin --image_type capture で標準入力のキャプチャー画像を推論するiinfer-mclient-ccapture|\iinfer-mclient-cpredict-n<モデル名>\--stdin\--image_typecapture\--nodraw|\iinfer-mpostprocess-cdet_filter-f-P\--stdin\--score_th0.8AIモデルのセッションを開放:# 画像AIモデルを停止させてセッションを開放iinfer-mclient-cstop-n<モデル名>-fLisenceThis project is licensed under the MIT License, see the LICENSE file for details
Package: iipyper
iipyperiipyperis a Python package for fast creation ofOpen Sound Control(OSC) andMIDI-based event loops.It is designed for creatives who want to explore the Python ecosystem and experiment with music and, for example, machine learning.For examples and tutorials of how to useiipyper, see ourexamples repo(TBC).Installiipypercan be installed viaPyPI:pipinstalliipyperDevelopgitclonegit@github.com:Intelligent-Instruments-Lab/iipyper.git
pipinstall-eiipyperContactiipyperis developed by theIntelligent Instruments Lab. Get in touch tocollaborate:◦iil.is◦Facebook◦Instagram◦X (Twitter)◦YouTube◦Discord◦GitHub◦LinkedIn◦Email◦FundingThe Intelligent Instruments project (INTENT) is funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 101001848).